Image Denoising Based on Multi-Stage Cascade Complementary Learning

Image denoising is a key component of digital image processing systems. The latest advances in deep learning have led to significant improvements in denoising techniques, particularly through complementary learning methods that integrate content learning and noise learning. However, existing complementary learning methods typically adopt parallel structures, where the content and noise learning branches run independently and their outputs are directly combined together. Therefore, the two learning methods cannot utilize the effective information in each other's outputs, which results in the information not being fully utilised. To overcome this limitation, we propose a multi-stage cascaded complementary learning (MSCCL) method, which employs a cascaded architecture of noise and content predictors to achieve effective complementary learning. Furthermore, a cross-stage feature fusion strategy is designed to enable effective integration of feature maps between the noise stage and content stage. Experimental results demonstrate that the proposed MSCCL method achieves superior denoising performance compared to previous approaches, under both additive white Gaussian noise and real-world noise conditions.