Blind super-resolution model based on unsupervised degenerate indication learning

Abstract

Blind super-resolution of images based on contrast learning has achieved better performance, which can distinguish between different degraded information. However, there is no significant improvement in the processing of image super-resolution. To further improve the performance of image blind super-resolution, a new degenerate higher-order attention network (DHAN) is designed for image super-resolution, in which a higher-order channel attention mechanism is introduced to the framework. Meanwhile, it also introduces normalized variance and global covariance pooling, which can provide higher-order feature information and more discriminative feature information. Comparing the experimental results, it can be found that the proposed higher-order attention network (DHAN) is able to distinguish various complex degradation patterns and thus obtain accurate degradation information. Numerous experiments have shown that higher-order attention network (DHAN) achieves good visual results in the image blind super-resolution task.