Superpixel Driven Unsupervised Deep Image Super-Resolution

Most of the existing deep learning-based image super-resolution methods require a large number of datasets or ground truth. However, these methods are not suitable for the restoration of real image with different domains. Recently, Deep Image Prior (DIP) based on single-image explores image prior and uses network structure as implicit image prior to recover images, but it ignores the explicit prior information of the actual image itself. The addition of image prior can effectively alleviate the ill-posed problem in the image restoration model. Therefore, in this paper, we propose an unsupervised deep image super-resolution (SR) method that based on segmentation driven. Intuitively, clear image has a clearer segmentation boundary. It will drive deep neural networks to obtain higher performance SR image when forcing the restored image to have clear boundary. In order to make energy flow into DIP better, we use the fully convolutional networks-based superpixel method, and we use back propagation to inject the gradient generated by segmentation entropy energy into DIP to obtain lower energy optimization parameters. Experiments show that the image generated by our method has clearer boundary and better performance than that generated by DIP on Set5, Set14 and BSD100.