PET-DSCFormer: Efficient Degradation-aware Transformer for Real-world Image Denoising

Image denoising is a fundamental task aiming to recover clean images from real-world degradations while preserving structural fidelity and fine-grained details. Recent CNN- and Transformer-based restoration methods have achieved remarkable progress by improving local representation learning and global context modeling. However, existing approaches still face three major limitations: dense Transformer attention is computationally expensive for high-resolution restoration, conventional skip fusion may propagate unreliable degraded features into the decoder, and most methods predict restoration residuals in a deterministic manner without explicitly modeling the uncertainty and complementarity of multi-scale residual estimates. To address these limitations, we propose PET-DSCFormer, a posterior error transport restoration framework for real-world image denoising and general image restoration. Extensive experiments on multiple public real-world denoising datasets, including medical imaging and natural image benchmarks, demonstrate that PET-DSCFormer achieves competitive or superior restoration performance compared with state-of-the-art methods. Moreover, experiments on GoPro deblurring further validate the generalization capability of the proposed framework beyond denoising. The code will be publicly available on GitHub upon acceptance.