Adaptive fusion super-resolution based on parallel scoring

Super-resolution image reconstruction refers to the estimation of high-resolution image from low-resolution input image. The reconstruction effect depends heavily on the relevance between the external training set and the test image. In this paper, we propose a novel fusion SR method, which makes use of the self-similar property of internal samples, so that the ill-condition external training set can also reconstruct the image robustly. Specifically, we define relevance quantitative index and calculate relevance scores for parallel feature channels. Based on the principle of consistency maximization of low correlation regions, we reasonably synthesize the results of parallel scores, classify image patches, and determine the fusion proportion of internal and external samples adaptively. Experimental results show that the proposed algorithm can effectively alleviate the visual defects caused by ill-conditioned training set. It is worth noting that under the condition of insufficient training samples, our method perform better than the reconstruction method based on deep learning.