# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 1 Introduction
With the emergence of large-scale datasets, low-light image enhancement based on deep learning has become the current mainstream technology. Supervised image enhancement methods use paired training data and leverage neural networks to learn mapping relationships from low-light images to enhanced images. The deep learning image enhancement method based on Retinex theory combines the classic Retinex image decomposition theory with deep learning methods to learn the mapping from the original image to the Retinex component, in order to achieve the purpose of image enhancement. [PERSON] et al. collected a low-light image dataset called LOL (Low-Light Dataset) and proposed the RetinexNet network on the LOL dataset [9]. The network comprises a Decom-Net for decomposition and an Enhance-Net for illumination adjustment composition. In low-light image enhancement, increasing only the area's brightness will amplify the image's hidden artifacts. LLDHusion proposed a joint learning framework of image generation and image enhancement to learn degraded representations and developed a dynamic diffusion module that simultaneously considered color mapping and potentially degraded representations to guide the diffusion process [10]. The PyD-iff algorithm proposes a pyramid diffusion model to enhance the image, sampling in a pyramid by gradually increasing the resolution during the back-propagation process [11]. The PyDiff algorithm uses a global corrector to alleviate the global degradation problem. Unsupervised methods do not require paired datasets, significantly improving research efficiency. [PERSON] et al. proposed a cyclic interactive generative adversarial network for unsupervised low-light image enhancement, which can better convey the illumination distribution between low-light images and real images [12]. The cyclic interaction process can effectively suppress synthetic real noise. Methods based on curve fitting have achieved good enhancement results in low-light image tasks. [PERSON] et al. proposed a lightweight deep network called Zero-DCE, which can iteratively apply approximation pixels and specific high-level Order curves, effectively mapping within a wide dynamic range and enhancing low-light images [13]. [PERSON] et al. proposed the Zero-DCE++ algorithm [14] based on Zero-DCE. Based on the original network structure, depth-separable convolution replaces the traditional convolution, and the input image is downsampled to estimate the curve. Then, upsampling is performed to restore the enhanced image, and the parameter amount and calculation amount of the improved model are significantly reduced, improving the model's inference speed. Although there are many methods to improve the quality of low-light images in existing low-light enhancement tasks, the enhanced images still need to be improved compared with standard images. On the one hand, the brightness of the captured image is low or uneven due to insufficient lighting or uneven lighting in the shooting environment. Uneven brightness will lead to significant differences between different areas of the image, with some regions being brighter and some incredibly dark, which poses a more substantial challenge to image brightness adjustment. On the other hand, in low-light conditions, the amount of incident light may not be enough to accurately capture the color and detail of a scene or object, and white balance may be affected, resulting in color deviations. For example, images taken under yellow light may show a yellow color cast. These factors can all lead to desaturated colors, insufficient contrast, and overall color distortion. Finally, the shooting equipment will perform a shutter delay in low-light shooting environments. These can easily cause blurred image content and unclear object edge structures. Reducing content blur while adjusting image brightness is also a research difficulty in low-light image enhancement.