Self-calibrated Attention Neural Network for Real-World Super Resolution.

Single Image Super-Resolution in practical scenarios is quite challenging, because of more complex degradation than bicubic downsampling and diverse degradation differences among devices. To solve this problem, we develop a novel super resolution network with large receptive field called SCA-SR. The contributions mainly contain the following four points. First, we introduce self-calibrated convolutions to low-level vision task for the first time to significantly enlarge the receptive field of SR model. Second, Cutblur methods are used to improve the generalization of model. Third, long skip connection was used in model design to improve the convergence of deep model structure. Fourth, we use both self-ensemble and model-ensemble to improve the robustness of model and reduce the noise introduced by individual model. According to the preliminary results of AIM 2020 Real Image Super-Resolution Challenge, our solution ranks third in both \(\times \)2 and \(\times \)3 tracks.