Advanced transformer for high-noise image denoising: Enhanced attention and detail preservation

In image denoising, the transformer model effectively captures global dependencies within an image due to its self-attention mechanism. This capability enhances the understanding of the overall structure and details of the image during the denoising process. However, the computational complexity of global self-attention increases quadratically with higher spatial resolutions, making it unsuitable for the real-time denoising of high-resolution and high-noise images. And, the use of local windows alone neglects the long-range pixel correlations. Furthermore, the self-attention mechanism applies a global weighting to the pixels of the input image, which can lead to the smoothing or loss of fine details. To enrich structural information and alleviate the computational complexity associated with global self-attention, we propose an edge-enhanced windowed multi-head self-attention mechanism (EWMSA). This mechanism combines edge enhancement with windowed self-attention to reduce computational demands while allowing edge features to better preserve detail and texture information. To mitigate the effects of ineffective features with low weights, we introduce a feed-forward network with a gate control strategy (LGFN). This network adjusts pixel weights to prioritize attention on effective pixels, thereby enhancing their prominence. Furthermore, to compensate for the limitations of window-based self-attention in global pixel utilization, we propose a deformable convolution block (DFCB). This block improves the interaction of contextual information and allows for better adaptation to texture variations within the image. Extensive experiments demonstrate that the proposed ATHID is competitive with other state-of-the-art denoising methods when applied to real-world noise and various synthetic noise levels, effectively addressing the challenges of high-noise image denoising. The code and models are publicly available at https://github.com/zzuli407/ATHID.