
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)11862-2
10.1016/j.heliyon.2024.e35831
e35831
Research Article
Natural low-illumination image enhancement based on dual-channel prior information
Wang Lingyun wanglingyun@kluniv.edu.cn

School of Big Data Engineering, Kaili University, Guizhou, Kaili 556011, China
08 8 2024
15 9 2024
08 8 2024
10 17 e358313 6 2023
30 7 2024
5 8 2024
© 2024 The Author
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
This paper proposes an adaptive image enhancement method that aims to effectively restore the brightness, detail, and natural color of various low-illumination images. To be specific, the method first constructs the initial dual-channel illumination map of the image. Next, the optimal illumination correction coefficient is calculated by the prior information entropy of the initial illumination map, which helps to correct potentially erroneous illumination estimates. To restore the illumination, gamma correction is used with the optimal illumination correction coefficient. Finally, an improved perfect reflection constraint model is used to restore the color of the image. Both visual analysis and quantitative comparison with state-of-the-art methods demonstrate the effectiveness of the method in terms of brightness adjustment, detail recovery, and color restoration.

Keywords

Low illumination
Image enhancement
Dual-channel
Information entropy
==== Body
pmc1 Introduction

As one of the most important factors for image quality, illumination presents the challenge of unpredictable visibility and color distortion. These issues not only weaken the visual presentation of images but also negatively affect the performance of computer vision tasks. Therefore, it is crucial to improve the quality of low-illumination images through image enhancement. This means that improved images can be used for various complex visual tasks.

Brightness migration has been the primary focus of various classic image enhancement methods over the past decades, such as Gamma Correction (GC), Logarithmic Transformation, and Histogram Equalization (HE). While these methods are easy to use, they do not take into account the actual features or brightness of the image [1]. To address this issue, Contrast Limited Adaptive Histogram Equalization (CLAHE) [2] adjusts the contrast by separating sub-histograms and establishing a noise-suppression threshold. Weighted Approximate Histogram Equalization (WAHE) [3] is an adaptive method for histogram equalization that looks natural and does not introduce visual artifacts. Contextual and Variational Contrast enhancement (CVC) [4] attempts to construct a new 2D histogram that takes into account the gray-level difference between each pixel and its neighbors. In contrast to CVC, Layered Difference Representation (LDR) [5] tries to find the statistical properties of the gray-level layers over a local region. Shanto et al. [6] suggested a method for dynamically selecting the gamma coefficient based on the features of the image provided to improve the contrast of each type of image. Despite all efforts, color distortion is unavoidable.

The dark channel prior (DCP) method [7] views a low-illumination image as a combination of the original image and a virtual haze image. It enhances the image illumination through the same transmittance estimation and recovery strategy as the dehaze process. The DCP method can effectively improve the visibility of image details and overall contrast. Salazar-Colores et al. [8] modified the dark channels to reduce periodic artifacts in the traditional DCP method. Sabir et al. [9] proposed segmenting images into sky and non-sky segments and used fog density to remove fog. Guo et al. [10] proposed an adaptive method to reduce halation in night vision images. The transmission function of the method is constructed by the relationship between the initial transmission and the critical gray value of halation. The DCP method includes several fixed parameters from the atmospheric dispersion model. This requires each image to be optimized explicitly, which limits the generalization performance and flexibility to some extent.

The Retinex theory [11] decomposes the image into reflection and illumination components. In this theory, the enhancement is focused on the illumination component and then merged with the reflection component to generate the enhanced image. Classical approaches, such as Single-Scale Retinex (SSR) [12], Multi-Scale Retinex with Color Restoration (MSRCR) [13], and Multi-Scale Retinex (MSR) [14], sometimes appear to be over-enhanced. Simultaneous Reflectance and Illumination Estimation (SRIE) [15] tends to incorporate the weight model to control illumination while optimizing the illumination map and reflection component. Guo et al. [16] modeled the illumination structure to adjust the illumination map. It considers the illumination component without the reflection component, which reduces the computational cost. Zhang et al. [17] introduced the regularization term of the illumination constraint in [16] to solve the over-exposure problem. However, this also noticeably slows the problem-solving rate.

There are other attempts to improve image illumination. Liu et al. [18] attempted to modify the illumination map by fusing multiple images. Qi et al. [19] applied a multilayer fusion strategy and a dual-interval frequency optimization illumination model. Salih et al. [20] distinguished the image into three distinct areas of underexposed, overexposed, and well-exposed areas based on the contrast degree distribution and accurately improved each area. Wang et al. [21] categorized the image into distant and near areas based on the depth of field. They used the bright channel to remove fog in distant areas, while the dark channel was used for near areas. Li et al. [22] divided the image into sky and non-sky regions. They used the bright channel to estimate the transmittance map of the sky region and the dark channel to estimate the transmittance map of the non-sky region, which created a haze-free and visually clearer image. Ning et al. [23] used a robust approach that included both bright and dark channels to remove haze from remote sensing images.

In recent years, with the rapid development of deep learning, Guo et al. [24] used a deep network to fit brightness mapping curves, which could adaptively estimate the optimal dynamic range adjustment curve suitable for image enhancement. It does not rely on paired data during training. Wang et al. [25] proposed a personalized image enhancement network that can communicate with users and reduce color distortion. Liu et al. [26] developed a deep learning framework that consists of a decomposition network and a adjustment network to integrate the data implicit prior and the traditional explicit prior. Golts et al. [27] introduced unsupervised deep learning networks that leverage a minimized dark channel prior as a loss function. This approach significantly enhanced the defogging results. Cui et al. [28] proposed an Illumination Adaptive Transformer (IAT) network that can handle all types of exposure, even overexposure. However, it may sacrifice some prediction accuracy due to its lightweight design. Deep learning can learn illumination features autonomously, which reduces the manual dependency, but it is difficult to optimize for brightness, noise, and contrast at the same time [29]. Better generalization [30] is still the focus of deep learning.

The proposed method aims to enhance brightness as well as maintain accurate detail and natural color. The analysis of current methods shows that it is easy to correct the image illumination with the traditional method. However, the brighter area may become overexposed before the darker area is properly illuminated in complex illumination. In addition, it is difficult to determine the correction coefficients of the image illumination when the illumination changes quickly. The presence of haze in the air is one of the primary causes of the above phenomena, and it is necessary to retain a certain amount of haze during removal. Furthermore, the parameters of the method remain constant when the illumination conditions are changed. In other words, it can be difficult to adaptively change the parameters in response to sudden changes in illumination. To address these issues, this paper proposes an image enhancement method for gamma correction based on dual-channel prior information. A single image (Fig. 1(a)) contains complementary previous information from both the bright channel (Fig. 1(b)) and dark channel (Fig. 1(c)). The bright channel contains more comprehensive prior structure information than the dark channel. However, the information on structure that is insufficient in the bright channel can be supplemented by the dark channel [31], which helps to correct potentially erroneous illumination estimates obtained from a single channel. Therefore, as shown in Fig. 1(d), this method attempts to combine the prior information from the bright and dark channels for low-illumination image enhancement, which can make the results more natural. The correction coefficient is determined based on the illumination information entropy in the dual-channel prior.Figure 1 Example of bright and dark channels: (a) Input Image (b) Bright Channel (c) Dark Channel (d) Proposed Method Results (e) Ground Truth.

Figure 1

The framework of the proposed method is illustrated in Fig. 2 and described as follows:• Capture the original illumination map of the image. Use the maximum filter to obtain the illumination map of the prior information of the bright channel in the image, and use the minimum filter to obtain the illumination map of the complementary prior information of the dark channel in the image.

• Refine the original illumination map. Calculate the difference between the bright and dark information entropy in the original illumination map to correct the potentially erroneous illumination information value estimation and produce a more accurate illumination map. Refine the image illumination from the final illumination map.

• Recover the image scene. The bright channel acts as a reference for the perfect reflection of the RGB channel to correct the color deviation of the image.

Figure 2 Framework of the Proposed Method.

Figure 2

The experimental results demonstrate that the proposed method effectively restores illumination, detail, and color. The contributions of the proposed method can be summarized as follows:• An adaptive image enhancement method based on dual-channel prior information entropy is proposed. It can be used to improve image illumination in various low-illumination scenarios.

• An image color cast method based on perfect reflection constraints is proposed for image color deviation correction.

• The performance of the proposed method was analyzed on four publicly available datasets and compared with several image enhancement methods. The results demonstrate the superiority of the proposed method over the compared methods.

The remainder of this paper is organized as follows: Section 2 provides a detailed description of the optimization method for low-illumination image enhancement. Section 3 describes the implementation of the experiment and reports the results. Finally, Section 4 summarizes the work of the paper and discusses future work.

2 Methodology

2.1 Estimating the original illumination map using both bright and dark channel priors

The illumination optimization method is based on the Retinex model, which assumes that the ideal image R(x,y) can be restored by improving the estimated illumination map T(x,y) from the original image I(x,y). R(x,y) is represented as follows (Eq. (1)):(1) R(x,y)=I(x,y)T(x,y)+ϵ

where ϵ is a very small constant that limits the ideal image R(x,y) to not being too saturated. (x,y) represents the horizontal and vertical coordinates of image pixels in the two-dimensional quadrant. Inspired by the theory of image dehazing [7], an inverted low-illumination image 1−Ic(x,y) is visually similar to haze images in terms of visual effect. The proposed method assumed that the inverted low-illumination image 1−Ic(x,y) is a pseudo-fog image Iˆ(x,y) and introduced the atmospheric scattering physics model [32], where the ideal image can be represented as (Eq. (2)):(2) Rc(x,y)=1−Iˆ(x,y)−α(1−T(x,y))T(x,y)+δ

where c∈(r,g,b) represents each of the three RGB channels. The constant α represents global atmospheric light, which is estimated by [7] to be higher than 0.95. In this work, α=0.98. The δ value is set to a very small number to ensure that the denominator is not zero. Obviously, whether Eq. (1) or Eq. (2) is used, an accurate illumination image T(x,y) is the key to further enhancing the original image I(x,y). The DCP method suggests that for a given image I(x,y), in a local low-illumination area (x,y) without sky or fog, at least one of the three RGB channels has the minimum light intensity, which is the dark channel prior Idark(x,y). Idark(x,y) can be formally represented as performing the minimum operation on the RGB channels independently (Eq. (3)):(3) Idark(x,y)=minc∈(r,g,b)⁡Ic(x,y)

DCP assumes that in the non-sky region of an ideal image without haze, the prior value of the dark channel approaches 0. Minimize the operations in Eqs. (2) and (3) as follows (Eq. (4)):(4) T(x,y)=1−βIdark(x,y)

where β(β∈(0,1)) is a parameter that establishes the level of recovery. Similar to the case of the DCP, the bright channel prior method [33] states that for a given image I(x,y), the brightest illumination on at least one color channel in the area of the image that is well-illuminated is in the form of (Eq. (5)):(5) Ibright(x,y)=maxc∈(r,g,b)⁡Ic(x,y)

Therefore, the normalized bright channel prior value should tend towards 1 in a perfect image without haze. To maximize operations, Eqs. (2) and (4) are combined as follows (Eq. (6)):(6) T(x,y)=1−1α(1−Ibright(x,y))

Theoretically, Eqs. (4) and (6) demonstrate the link between the bright and dark channels. However, it is important to note that while the bright and dark channels guarantee the existence of extreme values in most low-illumination areas of the image, the artificial transmission relationship between the dark background and the high-intensity illumination can easily introduce significant bias in the final result. As shown in Fig. 3(a), human factors like the bright light will obscure the original structure in the area. The proposed method aims to process every pixel more effectively and extract data that can accurately describe the image illumination. Due to the unclear spatial distribution of the abnormal illumination, numerous methods have been proposed to improve accuracy. The guided filter is used to preprocess the potential error transmission estimation of the prior illumination structure. Considering that prior information on the bright channel T˜bright(x,y) is closer to the assessment of global atmospheric light, it is used as a preliminary estimation of the illumination structure. In the same process, the prior information on the dark channel T˜dark(x,y) provides supplementary illumination information. It can be clearly observed that Fig. 3(d) represents an improvement in the quality of the enhanced image, which has uniform illumination and strong contrast.Figure 3 Illustration of the result of selecting the dual channel as the illumination map. (a) Input Image. (b) Bright Channel. (c) Dark Channel. (d) The Proposed Method Result.

Figure 3

2.2 Refining the illumination map using information entropy

The GC method was employed for the global dynamic range modification of the image. This method is commonly used to maintain the global brightness effect of low-illumination images while enhancing the image clarity. The function is consistently expressed as Eq. (7):(7) T(x,y)=T˜max(T˜(x,y)T˜max)γ

where T(x,y) is the corrected fine-illumination image of the original illumination map T˜(x,y), and T˜max is the maximum grayscale value. For instance, the maximum grayscale value is 255 for an 8-bit image. The correction parameter γ causes the dynamic values in the low-grayscale regions to change, making the image brighter when its value is less than 1. In contrast, the image darkens because the dynamic values in low-grayscale areas become smaller and those in high-grayscale areas become larger. The GC method is applied to the entire image. Therefore, when the image illumination information is complex and uneven, to ensure a natural and reasonable entropy value of contrast while effectively suppressing the influence of local noise points, the final illumination information γ is designed as (Eq. (8)):(8) γ=[lg⁡(T˜meanT˜max)/lg⁡(μ)]−1

where T˜mean represents the average reference brightness of the original illumination map T˜(x,y). For the bright channel, T˜mean selects the average intensity of the brightest 30% pixels in the image as the reference white average grayscale value. For the dark channel, T˜mean selects the darkest 30% pixel average intensity of the image as the reference black average grayscale value. μ as a correction factor is determined by the prior information entropy of the bright and dark channels, which prevents outliers of overestimated atmospheric light. As the information that exists in the image itself, information entropy can directly express the amount of bright and dark information contained in the image [34], [35], [36]. It is also a measure of the uncertainty of the illumination information in the image. There is a wealth of illumination data in the original illumination map of the bright and dark channels. The illumination information entropy is used to assess the distribution of the reference illumination values of the bright and dark channels. The value range is (0,1), and the correction coefficient μ of an image can be defined as (Eq. (9)):(9) μ=−∑n=0255pnlog2⁡pn

where pn is the probability of the occurrence of the reference brightness and darkness information grayscale value n(n=0,⋯,255) in an image. The gradient value of the image pixel is smaller, and the illumination information is insufficient when the illumination information entropy μbright in the bright channel is closer to 0. At this point, the image must be non-linearly adjusted to enhance the contrast between bright and dark conditions, increase details, and suppress noise interference. The gradient value of an image pixel is higher, and illumination information is sufficient when μbright is closer to 1. The image must reduce the contrast between bright and dark and suppress noise interference. By contrast, the illumination information entropy μdark in the dark channel is close to 0, which indicates that the illumination information of the image is sufficient. A value closer to 1 indicates that the illumination information of the image is insufficient. Consequently, the correction coefficient μ can ensure better processing of each pixel.

After calculating the illumination information γ=[γbrightγdark] that affects the entire image, as shown in Fig. 3, the unreliable illumination regions observed through the bright and dark channel priors (indexed by the red boxes in Fig. 3(b) and Fig. 3(c)) have missing structures in the illumination maps estimated solely through the bright and dark channel priors. The information entropy obtained from the dark channel is used to evaluate the darkness, and the information entropy obtained from the bright channel is used to evaluate the brightness. It can be seen that if the information entropy value of the bright channel is less than the information entropy value of the dark channel, then the image is almost dark or the image darkness information is large. In contrast, if the information entropy value of the bright channel is higher than that of the dark channel, the image is almost bright, or the image brightness information is large. In addition, the noise hidden in the dark areas will inevitably be directly amplified in GC. To prevent these problems, an upper bound γupper=1, and a lower bound γlower=0.01 are set for the illumination information γ. The natural illumination information entropy correction value γcorrect can be estimated by combining the differences between the bright and dark channels as follows (Eq. (10)):(10) γcorrect=min⁡(1−(1−min⁡γ)−φ(1−max⁡γ)max⁡(γ,γlower),γupper)

where (Eq. (11))(11) φ=min⁡γmax⁡γ

2.3 Recover the image scene under perfect reflection constraints

Once the estimated fine illumination map is obtained, it can restore the scene according to Eq. (2). It is important to keep in mind that 30% is used to assess the brightness and darkness of the information. Although much has been done, especially when measuring the darkness value, T(x,y) is always impacted by the noise that is buried in the low-illumination area. At this time, if T(x,y)→0, T(x,y)+ϵ will have a very high value. In addition, the maximum value of the RGB channels is chosen as the illumination map. With this approach, the maximum value of the RGB channels can somewhat control the illumination map. However, it is simple to produce a color deviation in the images. Therefore, a simple recovery method is achieved by constructing linear constraints that satisfy perfect reflection (Eq. (12)):(12) [∑Ibrightmax⁡Ibright]=[∑Ic2∑Icmax⁡Ic2max⁡Ic][uv]

where u and v are the correction coefficients of channel Ic, which requires correction. The parameters u and v are solved by the Gaussian elimination method of column principal components. It is believed that the bright channel Ibright(x,y) of the gamma-corrected image can be used as the perfect reflector. Therefore, the proposed method uses the bright channel as a reference for the color cast on the RGB channels of the image. The final image is generated by combining the color-corrected image and the gamma-corrected image.

3 Experiments

3.1 Implementation details

Four widely known image datasets were selected for qualitative and quantitative analyses. These datasets are all non-uniform illumination image pairs used to assess the efficacy of the method. The datasets were recorded under various illumination conditions. The large-scale real-world paired image dataset (LSRW) [37] consists of 5650 image pairings, of which 2480 were captured using a Huawei P40 Pro smartphone and 3170 using a Nikon D7500 camera. The LSRW dataset is the first large-scale real-world image pair dataset. The dataset constructed by the Single Image Contrast Enhancement (SICE) method [38] contains 589 high-resolution multi-exposure sequences. The least underexposed image sample in the SICE dataset was selected for the trial. The dataset constructed by the GLobal illumination-Aware and Detail-preserving Network (GLADNet) method [39] contains 5,000 pairs of composite images of various indoor and outdoor scenes. The last dataset used the same datasets as the Retinex decomposition network (Retinex-Net) method [40], which include 500 self-made pairs of real-world images named the LOw Light paired dataset (LOL) and 1000 pairs of the synthesized images from RAISE [41]. For comparison, six representative methods in the field of low-illumination image enhancement were selected, including CLAHE [2], CVC [4], LDR [5], SRIE [15], WAHE [3] and IAT [28]. The IAT method is based on deep learning. The other methods are traditional. The methods of CVC, LDR, SRIE, and WAHE were implemented on Matlab (CPU). The other methods were implemented on Visual Studio Code (CPU), which IAT has to run on GPU. All implementations of the method were according to the original papers. All experiments were conducted on a personal computer running the Windows 11 operating system with 64GB RAM, 3.8GHz CPU, and NVIDIA Quadro RTX 4000 GPU.

The experiments consisted of two parts: ablation experiments and comparison experiments with other state-of-the-art methods. Qualitative and quantitative analyses were performed for each experiment. The performances of these methods were quantified in a quantitative analysis using Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) as full reference image quality assessment metrics and the Contrast Enhancement based contrast-changed Image Quality measure (CEIQ) [42] as a non-reference image quality assessment metric. PSNR reflects the relative strength of noise and signal in an image. The greater the PSNR value, the closer the image quality matches the original image in terms of pixel values. SSIM is a quantifier of image similarity based on the comparison of brightness, contrast, and structure between two images. The value ranges between 0 and 1, where a higher value indicates a smaller structural difference between the reference and test images and a higher-quality test image. CEIQ provides quantitative image quality assessment results without a reference image. This measure represents the natural quality of an image, with higher values indicating a closer match with human subjective ratings.

3.2 Ablation experiments

To demonstrate the effectiveness of dual channels and color correction, three ablation experiments were designed to eliminate the bright channel, dark channel, and color correction components. In Fig. 4, it can be seen that although color correction has been applied to the input images (Fig. 4(a)) in the second column (Fig. 4(b)) and third column (Fig. 4(c)), the rest of the images still have varying degrees of exposure problems, except for the properly exposed Image 1. For example, Image 2, Image 6, and Image 8 in the second column are overexposed, with highlight overflow and detail loss. Image 5 in the second column and Image 6 in the third column are underexposed, with blurred details in the dark areas. In addition, Image 4, Image 6, and Image 8 in the fourth column (Fig. 4(d)) show significant reddish color distortion. In contrast, the proposed method (Fig. 4(e)) not only effectively solves the exposure problem but also ensures true color. It presents a natural and realistic visual effect similar to the Ground Truth (Fig. 4(f)).Figure 4 Visual comparison from the ablation experiments of the proposed method. (a) Input Image. (b) Result without Dark Channel Prior. (c) Result without Bright Channel Prior. (d) Result without Color Cast. (e) Proposed Method Result. (f) Ground Truth.

Figure 4

The visualization of qualitatively analyzed images can easily be influenced by the subjective awareness of individuals. To further validate the effectiveness of the experiment, the results of the quantitative analysis of the ablation experiments are listed in Table 1. The optimal values are shown in bold red, and the suboptimal values are shown in bold black. The proposed method outperforms all comparison results in terms of the CEIQ assessment metric, with improvements of 2.19%, 1.08%, and a remarkable 22.88% compared to the suboptimal result of the method without color cast on the GLADNet, LSRW, and Retinex-Net datasets. It indicates the great color correction and overall visual quality improvement performance of the proposed method. For the SSIM metric, the proposed method performs best on the LSRW dataset, with a slight improvement of 0.26% compared to the suboptimal result of the method without color cast. Furthermore, it performs second best on the GLADNet and Retinex-Net datasets, but its improvements are 16.99% and 61.40% compared to the third-best method. It shows that the proposed method is effective in maintaining the consistency of the image structure. In terms of PSNR, the proposed method achieves relatively high values on all test datasets. It performs 1.83% better than the suboptimal result of the method without color cast on the Retinex-Net dataset. It performs second best on the GLADNet and LSRW datasets and obtained improvements of 0.96% and 3.36% compared to the third-best method. This significantly shows that the proposed method is very good for recovering the original image information.Table 1 Data comparison of image quality assessment metrics from the ablation experiments of the proposed method.

Table 1METHOD	GLADNet	LSRW	Retinex-Net	
PSNR	SSIM	CEIQ	PSNR	SSIM	CEIQ	PSNR	SSIM	CEIQ	
Result without Dark Channel Prior	Image 1	0.653	2.707	Image 2	0.431	2.270	29.425	0.376	1.864	
Result without Bright Channel Prior	35.314	0.724	2.885	27.356	0.502	2.446	28.062	0.456	1.979	
Result without Color Cast	33.540	Image 3	3.148	29.418	0.755	2.972	29.547	Image 4	2.522	
Proposed Method Result	35.654	0.847	Image 5	30.405	Image 6	Image 7	Image 8	0.736	Image 9	

It is important to note that the results of the single-channel prior perform worse on both the SSIM and CEIQ metrics. The proposed method sometimes suffers from color bias, as shown in Image 6 of Fig. 4. The reason is that the gamma correction adjusts the global pixels without considering the interaction between colors. Furthermore, the proposed color correction method is based on perfect reflections, which are rare in reality. Therefore, color bias may occur when processing complex images.

Based on the analysis above, the fully implemented proposed method outperforms the non-fully implemented proposed method in terms of color accuracy and visual impact. The analysis led to the following conclusions:• The dual-channel prior can effectively correct potentially erroneous illumination estimates obtained from the single channel. This approach can help improve the overall illumination of the image.

• Color correction based on the bright channel can effectively improve the color bias and ensure visual accuracy.

3.3 Comparison with state-of-the-art methods

3.3.1 Qualitative analysis

Fig. 5 shows the comparison of the proposed method with other state-of-the-art methods in terms of subjective visual detail, with two magnified areas used to illustrate the visual differences. The first column shows Example 1, and the second and third columns show the corresponding enlarged, detailed images. Similarly, the fourth column shows Example 2, and the fifth and sixth columns show the corresponding enlarged, detailed images. It can be seen that all comparison methods do not achieve effective image enhancement compared to the proposed method.Figure 5 Comparison with other state-of-the-art methods in detail.

Figure 5

In the night scene in Example 1, the female area processed by CLAHE and IAT is overexposed. It causes a loss of detail. The other methods show varying degrees of underexposure. In terms of background detail restoration, all methods except the proposed method and IAT show insufficient detail restoration ability under weak signals. Although CLAHE improves image contrast to some extent, its color restoration effect is not natural. The proposed method improves brightness properly, preserves image detail, and restores color closer to the original image.

The daytime scene in Example 2 shows vague details of the black cat due to insufficient information provided by LDR, SRIE, and WAHE. Although CLAHE improves the image contrast, its color recovery is still not natural enough. For outdoor high-frequency detail, CLAHE tends to over-enhance the details, which causes noise and distortion. Meanwhile, IAT causes some details to be completely lost due to the over-enhancement effect. The other methods may recover some details better, but they still have some mismatches when compared to the ground truth image. In contrast, the proposed method successfully restored the details of the black cat in Example 2 and generated the best visible detail of the outdoor scene. Based on the above analysis, the proposed method produces visually pleasing results while avoiding overexposure. Other methods do not effectively enhance the visibility of dark details or over-enhance them.

For the overall subjective visual analysis, six low-illumination images were randomly selected from each dataset in this study. The low-illumination image enhancement results for each method are shown in Figure 6, Figure 7, Figure 8, Figure 9. Fig. 6 shows the enhancement results for the GLADNet dataset. Fig. 7 shows the enhancement results for the LSRW dataset. Fig. 8 shows the image enhancement results for the same dataset as that of Retinex-Net. Fig. 9 shows the image enhancement results for the SICE dataset (the IAT method was not implemented for the SICE dataset due to the insufficient performance of the experimental equipment).Figure 6 Comparison results of the different image enhancement methods on the GLADNet dataset.

Figure 6

Figure 7 Comparison results of the different image enhancement methods on the LSRW dataset.

Figure 7

Figure 8 Comparison results of the different image enhancement methods on the same dataset as the Retinex-Net.

Figure 8

Figure 9 Comparison results of the different image enhancement methods on the SICE dataset.

Figure 9

Both CVC and CLAHE can cause color distortion, which can make the image appear grayish and less vivid, such as Image 5 in Fig. 6 and Image 6 in Fig. 7. LDR is weak in terms of image brightness enhancement and limited in terms of color restoration and saturation, such as Image 2 in Fig. 7 and Image 3 in Fig. 9. On the other hand, SRIE and WAHE perform well in terms of overall image enhancement. However, there are still deficiencies to restore the natural and realistic color, such as Image 4 in Fig. 6 and Image 5 in Fig. 7. IAT can effectively enhance illumination, but it can also cause overexposure problems when it works on very bright areas such as the sky, as seen in Image 5 and Image 6 of Fig. 8. Additionally, IAT can cause color distortion problems, such as Image 3 and Image 4 in Fig. 7 and Image 4 in Fig. 8. In contrast, the proposed method not only improves the overall illumination, but also provides precise control for visual aspects of contrast adjustment, brightness optimization, and color restoration, such as Image 2, Image 4 in Fig. 6, and Image 4, Image 5 in Fig. 9.

The proposed method is able to simulate natural illumination effects even under complex and changing illumination conditions, such as Image 4, Image 5, and Image 6 in Fig. 8. The advantage of the proposed method is based on the Retinex theory, which is closer to the real perceptual needs of the human eye for visual information. It can provide a better overall visual experience for the observer.

3.3.2 Quantitative analysis

Table 2 shows the quantitative analysis results. The proposed method performs well on all four datasets. The PSNR assessment metric demonstrates that the proposed method outperforms all other methods on the SICE and LSRW datasets. Specifically, on the SICE dataset, the proposed method shows an improvement of 9.51% compared to the second-best method, CVC. On the LSRW dataset, the proposed method shows an improvement of 9.82% compared to the second-best method, IAT. The proposed method performs suboptimally on the Retinex-Net and GLADNet datasets. However, on the Retinex-Net dataset, the proposed method shows an improvement of 11.04% compared to the third-best method, LDR. Meanwhile, on the GLADNet dataset, the proposed method shows a remarkable improvement of 27.65% compared to the third-best method, SRIE. The results show that the proposed method effectively restores image information in low-illumination environments.Table 2 Quantitative results on the four datasets.

Table 2METHOD	SICE	Retinex-Net	LSRW	GLADNet	
PSNR	SSIM	CEIQ	PSNR	SSIM	CEIQ	PSNR	SSIM	CEIQ	PSNR	SSIM	CEIQ	
CLAHE	25.416	0.613	Image 10	25.655	0.595	2.818	27.109	0.647	3.013	27.231	0.777	Image 11	
CVC	26.076	0.653	2.960	26.255	0.642	2.562	25.610	0.644	2.957	27.421	0.802	3.136	
LDR	25.154	0.560	2.809	27.097	0.773	2.843	26.113	0.698	3.054	27.116	0.842	3.202	
SRIE	25.341	0.664	2.910	25.628	0.633	2.708	25.094	0.682	2.761	27.932	0.818	3.106	
WAHE	25.030	0.614	2.821	25.813	0.564	2.657	25.061	0.675	2.915	26.827	0.812	3.122	
IAT	-	-	-	Image 12	Image 13	Image 14	27.686	Image 15	Image 16	Image 17	Image 18	3.237	
OURS	Image 19	Image 20	2.971	30.088	0.736	3.099	Image 21	0.757	3.004	35.654	0.847	3.217	

In terms of the SSIM assessment metric, the proposed method shows the best result on the SICE dataset, with an improvement of 9.64% compared to the result of the second-best method, SRIE. Its performance on the LSRW and GLADNet datasets is suboptimal. Compared to the third-best method, LDR, the proposed method shows improvements of 8.45% and 0.59%, respectively. The results demonstrate the good performance of the proposed method in preserving the structural similarity of the images.

In terms of the CEIQ assessment metric, the results of the proposed method are suboptimal on the SICE and Retinex-Net datasets. Specifically, on the SICE dataset, the proposed method shows a slight improvement of 0.37% compared to the third-best method, CVC. On the Retinex-Net dataset, the proposed method shows an improvement of 9.01% compared to the third-best method, LDR. The results show the potential of the proposed method for color restoration and visual quality optimization.

The proposed method is divided into three major steps: (A) Capture the original illumination map of the image. The original illumination map is obtained by traveling through the entire image with a computational complexity of O(N), where N denotes the total number of pixels in the image. (B) Refine the original illumination map. The entropy of the bright and dark information is computed from the histogram of the original illumination map, with a computational complexity of O(N). Then, the gamma correction operation is implemented based on the information entropy, with a computational complexity of O(N). (C) Recover the image scene. The perfect reflection constraint is used to correct image color deviation with a computational complexity of O(N). The total computational complexity of the proposed method is O(N).

Table 3 shows the comparison of the average computation time of the different methods when processing a single image. The data were obtained by running the results for all images on the GLADNet dataset 10 times. It can be seen that the proposed method takes more time to compute than the other methods. The main reason for this is that the guided filter is used as an optimization tool.Table 3 Average computation time per image on the GLADNet dataset for different methods.

Table 3Method	Computation Times (ms)	
WAHE	Image 22	
LDR	7.983	
CVC	56.575	
OURS(without guided filter)	44.135	
OURS	78.514	

3.4 Discussion

The analysis of the two experiments shows that the proposed method is effective in terms of brightness adjustment, detail recovery, and color restoration. The proposed method not only improves the overall illumination but also recovers the detail, and even the high-frequency detail can be effectively recovered. In addition, the color is restored naturally. Compared to other state-of-the-art methods, the proposed method produces visual effects that are more satisfactory and more in line with the visual characteristics of the human eye. In terms of quantitative evaluation, the proposed method performs slightly worse than the IAT method. To be specific, in terms of pixel value similarity, image structure similarity, and image contrast, the objective data of the proposed method performs well compared to other state-of-the-art methods except IAT. In terms of computation time, the proposed method is not the fastest. However, it is important to note that the IAT does not perform well enough in qualitative analysis due to the limited generalization ability of deep learning. The IAT is prone to problems such as overexposure and color distortion, which are not in line with the visual characteristics of the human eye. The proposed method demonstrates superiority in balancing objective performance and subjective visual experience.

4 Conclusion

This paper proposes an image enhancement method that adaptively generates illumination parameters. The method is based on the traditional Retinex model to estimate illumination by combining prior information from the bright and dark channels. The color cast is generated based on the linear constraint of perfect reflectance. The experimental results demonstrate that the proposed method performs well visually overall while naturally enhancing the results in many low-illumination environments. Therefore, the effectiveness of the dual-channel prior is illustrated.

However, the proposed method optimizes global pixels with GC, which can lead to artifacts in the enhanced results. Furthermore, the guided filter improves the image quality, but it inevitably increases the computational burden. Future work will focus on exploiting deep learning to solve the artifact problem and refine images at different exposure levels, even at overexposure. There will also be more visual measures introduced to better simulate human vision and reduce computation time.

Ethics declarations

Informed consent was not required for this study because it used public image datasets and did not involve personal, private information or biomedical imaging data.

Additional information

No additional information is available for this paper.

CRediT authorship contribution statement

Lingyun Wang: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Acknowledgements

The research is supported by: Natural Science Research Foundation of 10.13039/501100010828 Guizhou Provincial Department of Education (Youth), Qianjiaohe KY Zi [2020] 178 . Technology Plan Foundation of the Qiandongnan Prefecture, Qiandongnan Kehe J Zi [2021] No. 43 .
==== Refs
References

1 Zhang Y. Zhang J. Guo X. Kindling the darkness: a practical low-light image enhancer Proceedings of the 27th ACM International Conference on Multimedia. MM '19 2019 Association for Computing Machinery New York, NY, USA 9781450368896 1632 1640 10.1145/3343031.3350926
2 Zuiderveld K. Viii.5. - contrast limited adaptive histogram equalization Heckbert P.S. Graphics Gems 1994 Academic Press 978-0-12-336156-1 474 485 10.1016/B978-0-12-336156-1.50061-6 https://www.sciencedirect.com/science/article/pii/B9780123361561500616
3 Arici T. Dikbas S. Altunbasak Y. A histogram modification framework and its application for image contrast enhancement IEEE Trans. Image Process. 18 9 2009 1921 1935 10.1109/TIP.2009.2021548 19403363
4 Celik T. Tjahjadi T. Contextual and variational contrast enhancement IEEE Trans. Image Process. 20 12 2011 3431 3441 10.1109/TIP.2011.2157513 21609884
5 Lee C. Lee C. Kim C.S. Contrast enhancement based on layered difference representation of 2d histograms IEEE Trans. Image Process. 22 12 2013 5372 5384 10.1109/TIP.2013.2284059 24108715
6 Rahman S. Rahman M.M. Abdullah-Al-Wadud M. Al-Quaderi G.D. Shoyaib M. An adaptive gamma correction for image enhancement EURASIP J. Image Video Process. 2016 1 2016 35 10.1186/s13640-016-0138-1
7 He K. Sun J. Tang X. Single image haze removal using dark channel prior IEEE Trans. Pattern Anal. Mach. Intell. 33 12 2011 2341 2353 10.1109/TPAMI.2010.168 20820075
8 Salazar-Colores S. Ramos-Arreguín J.M. Pedraza-Ortega J.C. Rodríguez-Reséndiz J. Efficient single image dehazing by modifying the dark channel prior EURASIP J. Image Video Process. 2019 1 2019 66 10.1186/s13640-019-0447-2
9 Sabir A. Khurshid K. Salman A. Segmentation-based image defogging using modified dark channel prior EURASIP J. Image Video Process. 2020 1 2020 6 10.1186/s13640-020-0493-9
10 Guo Q. Wang H. Yang J. Adaptive dark channel prior enhancement algorithm for different source night vision halation images IEEE Access 10 2022 92726 92739 10.1109/ACCESS.2022.3203183
11 Land E.H. The retinex theory of color vision Sci. Am. 237 6 1977 108 129 http://www.jstor.org/stable/24953876 929159
12 Jobson D. Rahman Z. Woodell G. Properties and performance of a center/surround retinex IEEE Trans. Image Process. 6 3 1997 451 462 10.1109/83.557356 18282940
13 Jobson D. Rahman Z. Woodell G. A multiscale retinex for bridging the gap between color images and the human observation of scenes IEEE Trans. Image Process. 6 7 1997 965 976 10.1109/83.597272 18282987
14 Rahman Z. Jobson D. Woodell G. Multi-scale retinex for color image enhancement Proceedings of 3rd IEEE International Conference on Image Processing vol. 3 1996 1003 1006 https://doi.org/10.1109/ICIP.1996.560995
15 Fu X. Zeng D. Huang Y. Zhang X.P. Ding X. A weighted variational model for simultaneous reflectance and illumination estimation Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016
16 Guo X. Li Y. Ling H. Lime: low-light image enhancement via illumination map estimation IEEE Trans. Image Process. 26 2 2017 982 993 10.1109/TIP.2016.2639450 28113318
17 Zhang Q. Nie Y. Zhu L. Xiao C. Zheng W.S. Enhancing underexposed photos using perceptually bidirectional similarity IEEE Trans. Multimed. 23 2021 189 202 10.1109/TMM.2020.2982045
18 Liu S. Zhang Y. Detail-preserving underexposed image enhancement via optimal weighted multi-exposure fusion IEEE Trans. Consum. Electron. 65 3 2019 303 311 10.1109/TCE.2019.2893644
19 Qi Y. Liu C. Wu H. Shao X. Wei J. A multi-layer fusion image enhancement method for visual odometry under poor visibility scenarios Int. J. Appl. Earth Obs. Geoinf. 115 2022 103118 10.1016/j.jag.2022.103118 https://www.sciencedirect.com/science/article/pii/S1569843222003065
20 Amer Mohammed Salih A. Al-Khannaq M. Hasikin K. Ashidi Mat Isa N. Adaptive local exposure based region determination for non-uniform illumination and low contrast images Alex. Eng. J. 61 12 2022 11185 11195 10.1016/j.aej.2022.04.023 https://www.sciencedirect.com/science/article/pii/S111001682200285X
21 Wang S. Yang T. Sun W. Lu X. Fan D. Adaptive bright and dark channel combined with defogging algorithm based on depth of field J. Sens. 2022 2022 5330876 10.1155/2022/5330876
22 Li C. Yuan C. Pan H. Yang Y. Wang Z. Zhou H. Single-image dehazing based on improved bright channel prior and dark channel prior Electronics 12 2 2023 299 https://www.mdpi.com/2079-9292/12/2/299
23 Ning J. Zhou Y. Liao X. Duo B. Single remote sensing image dehazing using robust light-dark prior Remote Sens. 15 4 2023 938 https://www.mdpi.com/2072-4292/15/4/938
24 Guo C. Li C. Guo J. Loy C.C. Hou J. Kwong S. Zero-reference deep curve estimation for low-light image enhancement Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020
25 Wang Y. Jiang Z. Liu C. Li K. Men A. Wang H. Shedding light on images: multi-level image brightness enhancement guided by arbitrary references Pattern Recognit. 131 2022 108867 10.1016/j.patcog.2022.108867 https://www.sciencedirect.com/science/article/pii/S003132032200348X
26 Liu X. Xie Q. Zhao Q. Wang H. Meng D. Low-light image enhancement by retinex-based algorithm unrolling and adjustment IEEE Trans. Neural Netw. Learn. Syst. 2023 1 14 10.1109/TNNLS.2023.3289626
27 Golts A. Freedman D. Elad M. Unsupervised single image dehazing using dark channel prior loss IEEE Trans. Image Process. 29 2020 2692 2701 10.1109/TIP.2019.2952032
28 Cui Z. Li K. Gu L. Su S. Gao P. Jiang Z. You only need 90k parameters to adapt light: a light weight transformer for image enhancement and exposure correction BMVC 2022 238
29 Tang H. Zhu H. Fei L. Wang T. Cao Y. Xie C. Low-illumination image enhancement based on deep learning techniques: a brief review Photonics 10 2 2023 10.3390/photonics10020198 https://www.mdpi.com/2304-6732/10/2/198
30 Zhang C. Bengio S. Hardt M. Recht B. Vinyals O. Understanding deep learning (still) requires rethinking generalization Commun. ACM 64 3 2021 107 115 10.1145/3446776
31 Shi Z. Zhu M.m. Guo B. Nighttime low illumination image enhancement with single image using bright/dark channel prior EURASIP J. Image Video Process. 2018 1 2018 13 10.1186/s13640-018-0251-4
32 Dong X. Pang Y.A. Wen J.G. Fast efficient algorithm for enhancement of low lighting video ACM SIGGRAPH 2010 Posters. SIGGRAPH '10 2010 Association for Computing Machinery New York, NY, USA 9781450303934 10.1145/1836845.1836920
33 Wang Y. Zhuo S. Tao D. Bu J. Li N. Automatic local exposure correction using bright channel prior for under-exposed images Signal Process. 93 11 2013 3227 3238 10.1016/j.sigpro.2013.04.025 https://www.sciencedirect.com/science/article/pii/S0165168413001680
34 Wang C. Ye Z. Brightness preserving histogram equalization with maximum entropy: a variational perspective IEEE Trans. Consum. Electron. 51 4 2005 1326 1334 10.1109/TCE.2005.1561863
35 Min B.S. Lim D.K. Kim S. Lee J.H. A novel method of determining parameters of clahe based on image entropy Int. J. Softw. Eng. Appl. 7 2013 113 120 10.14257/IJSEIA.2013.7.5.11
36 Agaian S.S. Silver B. Panetta K.A. Transform coefficient histogram-based image enhancement algorithms using contrast entropy IEEE Trans. Image Process. 16 3 2007 741 758 10.1109/TIP.2006.888338 17357734
37 Hai J. Xuan Z. Yang R. Hao Y. Zou F. Lin F. R2rnet: low-light image enhancement via real-low to real-normal network J. Vis. Commun. Image Represent. 90 2023 103712 10.1016/j.jvcir.2022.103712 https://www.sciencedirect.com/science/article/pii/S1047320322002322
38 Cai J. Gu S. Zhang L. Learning a deep single image contrast enhancer from multi-exposure images IEEE Trans. Image Process. 27 4 2018 2049 2062 10.1109/TIP.2018.2794218
39 Wang W. Wei C. Yang W. Liu J. Gladnet: low-light enhancement network with global awareness 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) 2018 751 755 10.1109/FG.2018.00118
40 Wei C. Wang W. Yang W. Liu J. Deep retinex decomposition for low-light enhancement British Machine Vision Conference 2018 British Machine Vision Association
41 Dang-Nguyen D.T. Pasquini C. Conotter V. Boato G. Raise: a raw images dataset for digital image forensics Proceedings of the 6th ACM Multimedia Systems Conference. MMSys '15 2015 Association for Computing Machinery New York, NY, USA 9781450333511 219 224 10.1145/2713168.2713194
42 Yan J. Li J. Fu X. No-reference quality assessment of contrast-distorted images using contrast enhancement arXiv:1904.08879 2019
