# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 3 Method
### Luminance Adjustment Module  
The goal of the Luminance Adjustment Module is to adjust the brightness of low-light images in order to obtain enhanced images. The principle of brightness adjustment is designed based on the perceptual characteristics of the human eye towards light. The human eye's perception of light is nonlinear, meaning that the perception effect of the exact brightness change varies under different brightness levels. Therefore, brightness adjustment needs to be achieved using a nonlinear method. We propose a simulation approach for nonlinear color space image transformation, which improves image brightness by modeling adaptive contrast and brightness adjustment parameters. In addition, low-light images have low brightness and exhibit uneven brightness. For images with uneven brightness, if the overall brightness is directly enhanced to the same extent, the enhanced image will have problems of local over-brightness or over-darkness. Adjusting images with uneven brightness to make the overall brightness of the enhanced image more consistent requires increasing the receptive field for learning, in order to obtain more comprehensive brightness distribution information. Therefore, to achieve the consistency goal of brightness adjustment, a convolutional brightness adjustment module is added to the image encoding, completed by convolutional layers and a nonlinear brightness adjustment module. This module can model global brightness adjustment parameters, thereby guiding the model to perform better brightness enhancement. On the other hand, many parts of the image contain light sources or shadows. If the same brightness adjustment operation is applied to all areas, it will cause the image to have local over-brightness or over-darkness problems. We also designed a brightness adjustment module based on non-overlapping window attention [24] to avoid uneven brightness after adjustment. The attention mechanism can weigh and sum the patches of each window so that the model can better focus on particular important regions and details in the image, while also paying attention to the relationship between global and local features related to brightness, capturing global brightness distribution information. Further, we adjust the areas that are too dark or too bright in the local area using the non-overlapping window attention mechanism to model the relationship between global brightness features and local brightness features, thereby reducing the degree of local over-brightness or over-darkness in the enhanced image. The structure of the Luminance Adjustment Module is shown in Figure 3.  
Figure 2: The structure of the basic blocks. Among them are the: (**a**) Basic Encoder Block; (**b**) Deblur Decoder Block; (**c**) Middle Block.  
As shown in Figure 3, we designed two types of Luminance Adjustment Modules: the Attention Luminance Adjustment Module (ALAM) and the Convolution Luminance Adjustment Module (CLAM). In ALAM, the illumination is adjusted using a Window Attention Block (WAB) based on non-overlapping sliding windows, where \(q\) is the learnable query, while \(k\) and \(v\) are simple copies of the output of the upper layer. WAB can capture contextual information in the image, model the relationship between windows, and capture the relationship between global illumination features and local illumination of the window, thereby obtaining an adaptive illumination-enhanced image. The Nonlinear Luminance Adjustment Block (NLAB) is a component common to ALAM and CLAM. In the NLAB, the image transformation process of gamma brightness correction is simulated, where \(\gamma_{1}\) and \(\gamma_{2}\) are two learnable matrix parameters. When initialized, the \(\gamma\) matrix is an identity matrix with all elements being 1.