PRNet: Low-Light Image Enhancement Based on Fourier Transform

Low-light image enhancement techniques constitute a significant approach for enhancing image brightness effectively while preserving image details. In this paper, PRNet is proposed, which is a novel lightweight low-light image enhancement network that leverages the Fourier transform, performing low-light image enhancement in two stages. In the first stage, a pixel enhancement network enhances the brightness of the lowlight image through a dense skip-connection structure. This structure incorporates a custom-designed Fourier-based brightness enhancement block. In the second stage, a refinement and restoration network processes the output from the first stage, further restoring image details. Detailed refinement is achieved using a dual-branch UNet structure, incorporating a bidirectional frequency domain cross-attention solver to optimize image quality. To thoroughly assess the performance of the proposed PRNet, nine well-established benchmark datasets were employed for detailed quantitative and qualitative evaluations. Experimental results show that PRNet achieves high quality image enhancement at significantly reduced computational complexity.