# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 3 Method  
### Framework  
Firstly, we introduce the overall network structure of our proposed LACCNet, as shown in Figure 1. The low-light image enhancement task is essentially an image generation task, so we apply a UNet-like network structure as the base network. The encoder is mainly composed of three Basic Encoder Blocks (BEBs) and 3 Luminance Adjustment Modules (LAMs). BEBs can extract features from the input image. LAM includes one Attention Luminance Adjustment Module (ALAM) and two Convolution Luminance Adjustment Modules (CLAMs). The Luminance Adjustment Module can adaptively adjust the image illumination and obtain luminance-adjusted image feature information. The neck structure consists of two Middle Blocks (MBs) that do not change the shape and size of feature maps. The Decoder Module is mainly responsible for multi-scale decoding of the image features extracted by the encoder and generating enhanced image content. Considering that the content of the input image is blurred, if the features extracted by the encoder are directly added to the decoder features, the content of the generated image will still be blurred. Inspired by LEDNet, we applied the Deblur Decoder Block (DDB), including a content deblur module based on dynamic filters [23], to obtain a brightness-enhanced image with clearer content. There is a residual connection operation between each group of Encoder Module and Decoder Module of the same size, represented by a blue line. Residual connections can both improve the stability of the network and also help the network better learn the nonlinear complex features of images. We also designed a Color Correction Module (CCM), including a Color Feature Extractor Block (CFEB), a Self-Attention Block (SAB), and a Learnable Color Matrix. The global color features of the shallow feature map of the input image are captured through the transformer structure, and color correction is performed before outputting the final enhanced image. In addition, on the basis of content loss and perceptual loss, we also introduce a color loss function to enhance the network's ability to reconstruct image color information and further improve the visual effect of the enhanced image.  
The structure of the Basic Encoder Block, Deblur Decoder Block, and Middle Block is shown in Figure 2. These blocks are mainly composed of the superposition of convolution and activation functions. The residual connection can prevent gradient vanishing and improve network performance. In the Basic Encoder Block, the downsampling of features is performed through convolution with a stride of 2. The Deblur Decoder Block adopts an interpolation operation for feature upsampling. In the Deblur Decoder Block, we use PReLU as the activation function, which can better prevent gradient vanishing problems and improve network stability compared to the ReLU function. Below, we provide a detailed introduction to the design of the Luminance Adjustment Module, Color Correction Module, and loss function in separate sections.  
Figure 1: The framework of the proposed LACCNet. A low-light image is used as input, a Luminance Adjustment Module is added to the encoder–decoder structure, a Color Correction Module is established to correct color distortion, and the final enhanced result image is output. The blue line represents the residual connection.