# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 1 Introduction
The MSR algorithm built upon the foundation of SSR by incorporating multiscale Gaussian filtering, leading to advancements in aspects such as color enhancement and dynamic range compression. But it still had issues such as inadequate edge sharpening, color distortion, and unclear details in bright regions, leading to visual discrepancies in the overall appearance of the enhanced images. The MSRCR algorithm improved the overall visual perception of images by introducing color restoration factors. However, the issue of noise remained prominent. [PERSON] et al. [9] proposed an image enhancement algorithm that utilizes a Poisson noise-aware Retinex model, effectively suppressing noise during the enhancement process and improving the clarity of image details. The low-light image enhancement algorithms based on the Retinex theory enhance the contrast of input images while also moderately mitigating the impact of noise. Nonetheless, the improvement in terms of brightness enhancement is somewhat constrained, and there is room for enhancing real-time performance and general applicability. Furthermore, these algorithms require manually inputting parameters based on prior knowledge, resulting in inconsistent enhancement effects for different types of images. Amid the progression of computer science and technology, deep learning has emerged as a predominant avenue of exploration within the realm of image enhancement. Employing this trend, the LLNet network [10] leverages a pre-trained deep autoencoder to extract signal characteristics from low-light images. The network's primary accomplishment is the enhancement of image contrast and the mitigation of noise. On the other hand, it also presented a notable issue of significant color distortion. MSR-Net [11] performs end-to-end enhancement by mapping low-light images to normal-light images, offering a novel perspective on the research of image enhancement algorithms. RetinexNet [12] first decomposes the input image into smoother illumination and reflectance components using a constrained loss function. It then enhances the illumination component and applies BM3D denoising. While the algorithm addresses color distortion issues, the enhanced images still lack sufficient clarity, thereby impacting the retrieval of fine details. KinDNet [13] proposes a method for continuously adjusting illumination, which has greater applicability compared to traditional gamma correction. However, the issue of excessive sharpening and blurred details still persists. EnlightenGAN [14] addresses the difficulty of collecting paired datasets by training a lighting enhancement network using unpaired data, effectively establishing the mapping correlation linking low-light images and reference images. Nevertheless, during the training process, challenges such as gradient vanishing and exploding tend to arise. [PERSON] et al. (2019) introduce a set of iterative mapping curves in their enhancement algorithm to increase the mapping's scope and improve the effectiveness of the enhancement. [PERSON] et al. (2019) employ generative adversarial networks to learn high-quality information from the dataset, resulting in improved overall image quality and enhanced details. [PERSON] et al. (2019) present an unsupervised learning-based algorithm for enhancing low-light images. By incorporating saturation loss and self-attention mapping, the algorithm successfully improves image detail clarity, while the challenge of noise reduction remains to be addressed. The conceptual framework of DCC-Net ([PERSON] et al., 2019) involves decoupling each color image into grayscale images and color histograms. These components are then separately enhanced to mitigate discrepancies in color and content consistency between the enhanced and actual images. The grayscale images contribute to generating accurate structures and textures, while the color histograms facilitate image color correction. URetinex-Net (2019) transforms the principle of image decomposition into an implicitly regularized model guided by prior rules.