Switching the Iteration in Alternating Optimization for Blind SuperResolution

To promote the super-resolution (SR) technology in real-world applications, the blind SR, involving kernel estimation and image restoration to super-resolve images with unknown degradation, has become one of the research focuses. Most existing methods either implement the above two tasks step-by-step so that do not well consider the compatibility between them, or repeatedly apply two modules over and over again to emphasize cooperation but limit the adaptive development of each one. Towards the above issues, based on the Deep Alternating Network (DAN), a novel training strategy named switching the iteration is proposed in this paper. In the first stage, an estimation module and a restoration module are optimized alternately to promote compatibility. In the second stage, duplicate the pre-trained modules and place them alternately to form a linear structure to promote adaptive development. Extensive experiments on isotropic Gaussian degradation datasets and irregular blur kernel degradation datasets show that the proposed method can achieve visually pleasing results and state-of-the-art performance in blind SR.