<p>Current diffusion model based image denoising methods suffer from semantic inconsistency and distortion due to their inability to maintain structural integrity, while diffusion models face two inherent limitations: (1) mandatory sampling initiation from pure Gaussian noise that mismatches denoising scenarios, and (2) uncontrollable uncertainty during stochastic sampling processes. Our work is motivated by the potential to harness precise edge information as conditional guidance to recalibrate diffusion sampling trajectories, thereby aligning the generative process with denoising objectives while preserving critical image structures. We propose a new image denoising framework, the EGDCF: Edge-Guided Diffusion networks with Coarse-to-Fine learning for image denoising, that synergizes edge-aware conditional guidance with a coarse-to-fine refinement mechanism through three key components: edge extractor, conditional diffusion model, and iterative denoising scheduler. EGDCF first uses a trainable Canny operator to extract multi-scale edge maps from noisy inputs, while calculating the number of denoising steps in the diffusion model’s reverse process based on the noise level of the input, thereby aligning the denoising trajectory with the actual noise distribution. Then, these structural priors are injected into the modified U-Net backbone network through attention based feature fusion, thereby guiding the sampling trajectory of the diffusion model’s reverse process. Finally, by fusing the noise input with the denoising results as the input for a new iteration, a clean denoised image can be obtained after multiple iterations. Quantitative evaluations on Gaussian and real-world noise datasets show effectiveness of our proposed method compared to state-of-the-art methods, particularly effective in high-noise regimes where conventional approaches fail.</p>