A simple but effective and efficient global modeling paradigm for image restoration

Global modelling-based image restoration frameworks (e.g., Transformer-like architecture) has gained popularity. Despite the remarkable advancement, the success may be at the cost of model parameters and FLOPs while the intrinsic characteristics of specific task are ignored. The objective of our work is orthogonal to previous studies and we thus tailor a simple yet effective global modelling paradigm for image restoration. The key insights which motivate our study are two-fold: 1) Fourier transform is capable of disentangling image degradation and content component, acting as the image degradation prior embedded into image restoration framework; 2) Fourier domain innately embraces global property where each pixel of Fourier space is involved with all the spatial pixels. We obey the de facto global modeling rule ``spatial interaction + channel evolution" of previous studies. Differently, we customize the core designs: multi-scale Fourier period spatial modeling and Fourier channel evolution. Equipped with above designs, our image restoration paradigm is verified on mainstream image restoration tasks including image de-raining, image enhancement, image de-hazing, and guided image super-resolution. The extensive experiments suggest that our paradigm achieves the competitive performance with fewer computational resources. Our main focus is not to beat previous frameworks but hopes to provide an alternative global modelling-based customized image restoration framework. Code will be publicly available.