# Multi-Feature Guided Low-Light Image Enhancement
## 2 Related Work  
From the emergence of digital image processing technology to the present, image enhancement technology has made considerable progress. In recent years, with the rise of target detection, more and more low-light image enhancement and denoising algorithms have been proposed. Enhancing the image by changing the contrast is an early classic idea, such as histogram equalization (HE) and an improved version of the contrast-limited adaptive histogram equalization (CLAHE) [10]; the principle is to increase the contrast by expanding the dynamic range of image pixel values and reducing the image gray level. Another method is gamma correction (GC), which changes the contrast by multiplying each pixel. The limitation of these two types of methods lies in the global adjustment of the image, and the resulting image will be distorted. Retinex theory [11] believes that the object has color constancy, and the image can be expressed as reflection and illumination. Early methods based on this theory include single-scale Retinex (SSR) [12] and multi-scale Retinex (MSR) [9]. The output of this method tends to be unnatural and locally over-enhanced. [PERSON] et al. [8] proposed a weighted change model to estimate the reflection and illumination of the input image. In the same year, they proposed an image enhancement method based on the fusion idea [6], which adjusted the illumination image through multi-scale fusion and weighted average strategy. [PERSON] et al. [13] adjusted the image by combining texture and illumination priors. [PERSON] [4] designed a structure-aware smoothing model to predict the illumination map. [PERSON] et al. [14] proposed a robust Retinex model, which introduced noise feature maps for denoising enhancement. The method based on Retinex theory is to estimate the illumination map by decomposing the image, and then enhancing the illumination map. This kind of method  
Figure 1: Comparison of image enhancement model results: (**a**) is the corresponding low-light image, (**b**–**h**) are the enhanced result of other methods, and (**h**) is the deep learning technique. depends on the accuracy of image decomposition, but often the decomposed components are not exactly the same, so the enhanced image obtained is less authentic, and noise will inevitably be added and amplified. Deep learning methods have shown unprecedented potential in learning image enhancement. [PERSON] et al. [4] combined Retinex and deep learning methods to propose Retinexnet. This network decomposes the image, and under the assumption of smooth illumination map, the reflection component is denoised by the BM3D method, and finally the image is reconstructed. However, this method will cause color distortion and unsatisfactory noise removal effect. [PERSON] et al. [15] introduced an intermediate illumination layer in the network for image enhancement and learned the image adjustment ability through image data carefully designed by experts. [PERSON] et al. [16] proposed an image enhancement method for processing unpaired datasets based on a two-way generative confrontation network. [PERSON] et al. [17] performed most of the processing at low resolution and used deep bilateral models for fast image enhancement. [PERSON] et al. [1] proposed a fully convolutional network model that can be trained end-to-end for processing low-light images. As low-light image enhancement is an ill-posed problem, no ground truth is available in the real situation. Therefore, there are many deep learning methods for low-light image enhancement based on unpaired or self-supervision. [PERSON] et al. [2] set a series of non-reference loss functions to enable the network to perform end-to-end training without any reference images. [PERSON] et al. [18] proposed an efficient and unsupervised generative adversarial network called EnlightenGAN for the low-light image enhancement problem, which can be trained without low/normal-light image pairs. [PERSON] and [PERSON] [19] used an adaptation of the CycleGan structure to color and denoise images.