Unsupervised simple Siamese representation learning for blind super-resolution

(DRAN) for image SR. In which, we propose the use of a simple Siamese representation learning to extract the degradation information from various LR images. Specifically, DRAN distinguishes degradation information instead of performing degradation estimation, which can greatly reduce the difficulty. In other words, DRAN can avoid pixel-level operations, transform degradation computation problems into degradation classification problems and flexibly process LR images through degradation representation learning. Finally, DRAN also introduces a channel attention mechanism to enhance the performance of SR. Experimental results show that the proposed scheme can distinguish different degradation modes and obtain accurate degradation information. Meanwhile, experiments on synthetic and real images show that the DRAN achieves remarkable performance on blind SR tasks with good visual effects.