# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 1 Introduction  
Insufficient illumination, environmental factors, or technical limitations of equipment often result in low-light images. The captured images and videos have problems such as inadequate brightness, low contrast, and color distortion. However, with the progress of society and the continuous evolution of people's lifestyles, it is necessary to capture images and videos in various scenes with insufficient lighting. For example, people often record their travels at night. Urban surveillance may conduct person searches, vehicle tracking, or evidence collection for illegal and criminal activities, especially when lighting is insufficient or at night. Additionally, when shooting in memorable scenes such as underwater or in mines, most images and videos suffer from inadequate lighting. As one of the primary image quality enhancement issues, low-light image enhancement technology aims to improve the visual quality of such low-light images and restore lost image information. This technology has been widely used in medical image processing, autonomous driving, industrial inspection, and other fields, thereby attracting the attention of researchers. Low-light image enhancement algorithms can be divided into two categories: low-light image enhancement methods based on traditional techniques and deep learning-based low-light image enhancement methods. Low-light image enhancement methods based on traditional techniques generally include the following approaches: histogram equalization algorithms, transmittance estimation-based algorithms, Retinex decomposition-based algorithms, and fusion-based algorithms. The histogram equalization method increases contrast and highlights some details by expanding thecoverage of grayscale values, but excessive enhancement will cause the loss of image details. Local histogram equalization [1] considers the local characteristics of the image and applies histogram equalization to different areas of the image to preserve image details. Dynamic histogram equalization [2] segments the image histogram according to local minima, specifies a specific gray level range for each segmentation, and performs equalization operations separately, reducing additional noise introduced during the contrast enhancement. Enhancing low-light images using algorithms for transmittance estimation is inspired by the image dehazing task [3]. [PERSON] et al. refined the transmittance and optimized the atmospheric light value, which solved the noise and edge problems well and provided good theoretical support for low-light image enhancement [4]. Retinex theory decomposes images into reflective and illumination components to achieve a stable perception of color. The single-scale Retinex algorithm [5] generally uses Gaussian filtering to filter out the low-frequency incident components, and then the reflection components can be calculated for subsequent processing. MSR [6] uses multiple scale Gaussian filter operators based on the SSR method and then weights the final result. Based on the ideas of Retinex theory, researchers have also explored solutions combined with other low-light enhancement methods. For example, [PERSON] et al. proposed a low-light illumination estimation algorithm to model illumination, find the maximum value in the three channels of RGB to estimate the illumination of each pixel individually, and then add it to the initial illumination map [7]. A lighting estimation coefficient is used to adjust the lighting of each pixel as the final lighting map. [PERSON] et al. focused on noise and designed a Low-Rank Regularized Retinex Model to add a low-rank prior to the Retinex decomposition process to suppress the noise in the reflection image [8]. This method has led to great improvements in both image and video enhancement. The above-mentioned traditional techniques generally require many mathematical skills and rigorous mathematical derivation. The derived iterative process is generally complex and is more conducive to practical applications.