Physics-guided deep learning framework with attention for image denoising

Shengjun Liu, Ting Zhang, Ruoxi Deng, Xinru Liu, Hanchao Liu

Abstract: Deep neural networks have achieved remarkable success in image denoising, yet their design is predominantly empirical without clear theoretical guidance. Recent studies have revealed connections between neural networks and physics-based differential equations, offering a reliable guideline for network designs. Nevertheless, most of the theories used to guide the design of the model are not specific to the task, the mismatch between the guiding theory and the specific task will undoubtedly undermine the suitability and strength of data-driven models in specific scientific applications. To address this, we propose a novel physics-guided learning framework by incorporating the structure of the physics-based differential equations specialized for image denoising into the advanced deep model. Our framework features an asymmetric multi-scale U-Net architecture, combining an attention-based encoder with a physics-guided decoder and loss function. Experimental results show that our approach not only surpasses state-of-the-art methods in both Gaussian and real noise removal tasks, but also reduces the model’s reliance on large datasets.

External IDs:dblp:journals/vc/LiuZDLL25