# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 1 Introduction  
With the rapid development of intelligent information and related technologies, people's daily lives and work increasingly rely on the transmission of information. As the most common form of information presentation, digital images help humans store, transmit, and analyze information, and have become an indispensable part of human communication. During the process of image acquisition, low-light images are often obtained due to factors such as poor lighting conditions during image capture and limitations in hardware facilities of intelligent capture devices. The images themselves exhibit overall darkness, blurred image details, and poor contrast, which not only increase the difficulty of obtaining image information but also diminish their usefulness in subsequent tasks such as image classification and segmentation [1]. For such low-light images with multiple issues, it is necessary to employ image enhancement techniques to improve brightness and contrast, thereby obtaining more valuable information and facilitating further processing in computer vision systems. To tackle the mentioned concern, this investigation focuses on the exploration of algorithms aimed at enhancing low-light images. During the early stages of image enhancement technology development, spatial domain image enhancement and frequency domain image enhancement were the two most commonly used approaches. Frequency domain image enhancement involves enhancing low-quality images using two-dimensional Fourier transforms, such as homomorphic filtering, high-pass filtering, and wavelet transformations. On the other hand, spatial domain image enhancement techniques involve filtering, smoothing, and sharpening operations applied to each neighborhood of image pixels, such as linear enhancement and histogram equalization [2]. Low-light image enhancement algorithms based on histogram equalization effectively improve image brightness while suppressing image noise. However, these algorithms still suffer from issues such as underexposure, overexposure, or color distortion in certain local regions of the output image. The original purpose of image dehazing algorithms was to enhance images captured in foggy weather conditions. However, [PERSON] et al. [3] discovered a substantial similarity between inverted low-light images and images captured in foggy conditions. Consequently, they applied image dehazing algorithms to process the low-light images, leading to the proposition of a low-light image enhancement algorithm based on the dark channel prior theory. While this algorithm yielded a certain level of enhancement, it introduced pronounced edge exaggeration and evident object-background segmentation artifacts in the enhanced images, thereby presenting new avenues of investigation for subsequent researchers. Later, [PERSON] et al. [4] employed adaptive parameters to perform dehazing operations on blurry images, effectively addressing issues related to edge segmentation and enhancing the contrast of the resulting output images. While the low-light image enhancement algorithm rooted in dehazing models does enhance image brightness and contrast, the algorithm's physical model lacks comprehensive experimental validation. Moreover, the improved images often exhibit reduced clarity of details, ultimately falling short of producing satisfactory visual outcomes. In the late 20 th century, [PERSON] et al. based their work on the Retinex theory [5] and subsequently proposed several algorithms: the Single Scale Retinex (SSR) algorithm based on center-surround operations [6], the Multi-Scale Retinex (MSR) algorithm [7], and the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm that incorporates color restoration factors [8]. The SSR algorithm calculated the grayscale value by weighting the surrounding pixel values of a target point and then removed the illumination component using a Gaussian function to achieve image enhancement.