#Image Denoising

The Cross Aggregation Transformer enhances image restoration by incorporating innovative features such as Rectangle-Window Self-Attention and the Locality Complementary Module, improving on traditional Transformer models. This approach surpasses standard methods by overcoming the limitations of local square windows through horizontal and vertical rectangle attention and cross-window feature aggregation. The Axial-Shift operation further refines window interaction, supporting long-range dependencies. Furthermore, integrating the CNN's inductive bias into the Transformer allows for a hybrid global-local interaction framework, showcasing superior performance in extensive testing compared to recent state-of-the-art methods.

Awesome-Denoise explores advanced denoising techniques across diverse noise models such as AWGN, PG, and GAN, targeting RGB, raw, and hybrid color spaces. It emphasizes practical applications in single, burst, and video contexts, and features benchmark datasets like SIDD and RENOIR. This project is an invaluable resource for researchers and developers aiming to enhance image quality via self-supervised learning and ISP applications.

Discover how NAFNet transforms image restoration with a simpler and more efficient approach by removing complex nonlinear activations. Achieve exceptional results in deblurring and denoising tasks on established benchmarks like GoPro and SIDD while reducing computational costs.