# Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
## 2 Related Work  
Numerous techniques have been proposed for enhancing low-light images. Algorithms based on histogram equalization have derived dynamic histogram equalization (HE), adaptive histogram equalization (AHE) [7], and contrast-limited adaptive histogram equalization (CLAHE) [8], among others. HE is a process that increases the contrast of an image by distributing its gray range more evenly. AHE divides the image into several small blocks for histogram equalization processing, which can better improve the local contrast of the image and achieve better enhancement results. CLAHE sets a threshold and only averages the parts exceeding the threshold to each gray level, eliminating the blocky effect that may be caused by AHE. Traditional Retinex-based methods mainly include the single-scale Retinex method (SSR), multi-scale Retinex method (MSR) [9], and  
Figure 1: Enhanced effect of the traditional methods.  
Figure 2: A flowchart of the proposed method.  
others. SSR is carried out by using filters, which are designed to filter the various channels of an image. They are then used to create the illumination image. The low-light image and the illumination image are logarithmically processed and subtracted to achieve the enhancement effect. MSR is equivalent to stacking multiple SSRs, which can be regarded as the weighted sum of multiple SSRs. MSRCP adds color balance, normalization, etc., to the multi-scale MSR results. Low-light image enhancement based on dehazing applies the dehazing algorithm to the reflection image of the low-light image, and after processing the image, it is inverted to obtain the final enhancement effect.  
LLNet [11] presents a method that trains a self-encoder using a synthetic image dataset. It achieves noise reduction while still enhancing the image. [PERSON] et al. [12] proposed a network that aims to enhance low-light images using an unsupervised model known as LE-GAN. The network uses a light-aware attention module to reduce the noise and extract various features. They also introduced a loss function that can be used to address the issue of overexposure. [PERSON] et al. [13] proposed a self-calibrating lighting framework that features a lighting learning process with crowd-share for enhancement purposes. In contrast, the proposed self-calibrating lighting framework can significantly reduce computational costs and achieve fast processing. [PERSON] et al. proposed RetinexNet, which combines the Retinex theory with convolutional neural networks to enhance low-light images by enhancing the illumination image and reducing noise in the reflectance image using BM3D. R2 RNet [6] is also based on Retinex and deep learning networks, and it designs a residual module (RM) and adds it to Its Decom-Net, noise reduction network, and augmentation network to obtain better component results. Some algorithms combine Retinex with zero-shot networks, such as RRDNet [14]. RRDNet is modeled on the theory of Retinex and features the method by decomposing the low-illumination image into illumination, reflection, and noise images, thus achieving noise reduction. The method converts the Decom-Net into a generative network, thus reducing the coupling between the illuminance and reflection images.  
The key to the Retinex method is to obtain higher quality illumination and reflection images. The residual network has shown remarkable performance in various image processing tasks due to its unique jump connection structure, which effectively prevents the gradient from exploding or disappearing during the training of convolutional neural networks. We build on this foundation by integrating the residual network into the decomposition network to obtain better decomposition results. Furthermore, we introduce an improved color invariant loss function for enhancing the output. Figure 3 demonstrates the decomposition and enhancement results of the shimmering images.