DSU-Net: A Dynamic Stage Unfolding Network for high-noise image compressive sensing denoising

Deep unfolding networks (DUNs) have demonstrated considerable efficacy in the domain of compressive sensing (CS), attributed to their superior performance and interpretability. Nonetheless, many existing CS methodologies fail to account for the influence of noise prior to image sampling, resulting in blurring and distortion in the reconstructed images. To address these issues, this paper proposes a dynamic stage unfolding network (DSUNet). Firstly, a novel dynamic stage unfolding mechanism is proposed to achieve feature refinement by dynamically optimizing various noisy images in a sequential manner. Secondly, a step inertia fusion module (SIFM) is developed to execute multi-tiered information fusion from adjacent stages, thereby promoting feature reuse and minimizing the loss of detailed information. Finally, a step cross transformer denoiser (SCTD) is designed to capture correlations between distant pixels, effectively addressing the limitations associated with local attributes and significantly improving image reconstruction performance. Comprehensive experimental evaluations indicate that the proposed DSUNet achieves outstanding performance under both low CS ratios and high noise levels, successfully addressing the challenges of denoising and reconstruction in high-resolution and high-noise images. Codes are available at https://github.com/zzuli407/DSU-Net.