Searching Models with Nested Attention for Blind Super-Resolution

Blind super-resolution task aims to restore low-resolution im“ages with unknown degradations to high-resolution counter-parts. Existing methods rely on degradations estimation to re-construct high-resolution images. However, they need human involvement to obtain the best results as they treat unknown types of degradations as known conditions and manually select corresponding trained models. Moreover, they cannot fully use estimated degradations and generate blurry artifacts as they ignore that the impact of degradations on images is re-lated to images contents. In this paper, we propose HIS-NEST which contains an automatic search strategy HIS and a net-work structure NEST. Specifically, to bypass manual partici-pation, HIS automatically selects the clearest image by esti-mating the qualities of generated images. Furthermore, NEST protects the connection between degradations and images by using no loss functions to limit the degradations estimation and analyzing degradations from the perspective of channel and space. Extensive experiments show that our method out-performs state-of-the-art methods.