Abstract: A low light image enhancement method based on color restoration transformer networks (CRTNet) is proposed to address the issue of image and color distortion in low light environments. This method combines channel attention and spatial attention mechanisms. CRTNet consists of a color attention module (CAM), a color map module (CMM), and a sequential enhancement structure. Firstly, CAM is divided into two parts: color channel attention module and color space attention module. Utilizing the global information capture capability of Transformer, the color channel attention module emphasizes meaningful color channels by assigning higher weights to generate channel attention vectors. The color space attention module uses a three-layer convolution structure, focuses on spatial details in high-dimensional space and generates spatial attention weight map. Secondly, CMM extracts high-dimensional image features through a linear fitting process, scaling and shifting these features in the 64D space across both channel and spatial dimensions to obtain global and detail image information. By combining with the original image features, it supplements the color, brightness, contrast, and detail information in the original image features to achieve color enhancement. Finally, a sequential enhancement structure is adopted to repeat CAM and CMM operations three times with the output of CMM serving as input, in order to fit higher-order function mappings and effectively enhance low light images. Experiments results and user studies on public datasets demonstrate that the proposed method outperforms existing approaches in quantitative measurement, detail and color restoration.

Key words: low light enhancement, enhancement of lightweight images, color recovery, enhancement of image details, Transformer