U-Shaped Transformer Image Denoising Network with Decoder-Side Pre-Fusion Calibration

U-shaped encoder-decoder architectures are widely adopted for image denoising due to their strong capability in multi-scale modeling. However, most existing methods still rely on relatively fixed encoder-decoder feature fusion strategies, which limit effective collaboration between encoder and decoder features. To address this issue, we propose a U-shaped Transformer image denoising network with decoder-side pre-fusion calibration (UT-DPFC). Built on a U-shaped Transformer backbone, UT-DPFC inserts Structure-Detail Candidate Attention (SDCA) before encoder-decoder feature fusion at three decoding stages to calibrate decoder features, thereby providing a more reliable basis for fusion and facilitating the effective use of injected detail cues during subsequent restoration. The Transformer architecture alternately adopts Window-based Multi-Head Self-Attention (W-MSA) and Shifted Window-based Multi-Head Self-Attention (SW-MSA), together with a Locally Enhanced Feed-forward Network (LeFF), to enhance local feature modeling. SDCA generates queries from the current decoder features and performs content-adaptive retrieval and aggregation over a complementary key-value candidate pool composed of Multi-Scale Region Candidates (MSRC) and Multi-Receptive-Field Pixel Candidates (MRPC), thereby calibrating decoder features before fusion. Extensive experiments show that UT-DPFC achieves favorable performance on synthetic Gaussian and real-world denoising. It performs better in heavy noise and complex textures, maintaining moderate inference cost.