QLight-Net: Quaternion based low light image enhancement network

Images captured at night suffer from various degradations such as color distortion, low contrast, and noise. Many existing methods improve low-light images may sometimes amplify noise, cause color distortion, and lack finer details. The existing methods require larger number of parameters, which limits the adoption of these methods in vision-based applications. In this paper, we proposed a QLight-Net method to achieve a better enhancement with a comparably lower number of parameters. We proposed depth-wise quaternion convolution, and quaternion cross attention to develop the two-branch architecture for low-light image enhancement. The proposed model leverages gradient branch to extract color-aware gradient features. Further, It uses color branch to extract gradient-aware color features. The proposed method achieves an LPIPS score of 0.047, which surpasses the previous best results with lesser parameters, and achieves 0.88 and 29.05 scores of SSIM and PSNR, respectively. Our approach achieves a balance between computational efficiency and better enhancement.