Superpixel Guide for Transformer Low-Light Image Denoising Method

Existing low-light image denoising methods mainly use the feature extraction and denoising mechanisms of Transformer and Convolutional Neural Networks (CNN). They face two problems: the self attention mechanism based on local windows fails to fully capture the nonlocal self-similarity in images, and the calculation of self-attention in the channel dimension does not fully utilize the spatial correlation of images. To address these issues, this study proposes a superpixel guided strategy for a window partition-based visual Transformer method; the strategy can adaptively select relevant windows for global interactions. First, a Top-N Cross Attention mechanism (TNCA) is designed based on window interactions, the top N windows that are most similar to the target image window are selected dynamically, and the information related to the image windows in the channel dimension are aggregated, fully considering the nonlocal self-similarity of the image. Second, through superpixel segmentation guidance, the expressive power of local features within the window is significantly improved while enhancing the correlation of spatial features in the channel dimension. Finally, a hierarchical Adaptive Interaction Superpixel Guide Transformer (AISGFormer) is constructed. Experimental results show that AISGFormer achieves a Peak Signal-to-Noise Ratio (PSNR) of 39.98 dB and 40.06 dB on the SIDD and DND real image datasets, respectively. Compared with other advanced networks, the PSNR improves by 0.02 dB—14.33 dB and 0.02 dB—7.63 dB, respectively. AISGFormer interacts with local and global information and details more effectively, and it adaptively utilizes self-similarity to suppress region similarity noise.