We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable. Different from traditional super-resolution formulation, the lowresolution input is further degraded by noises and blurring. This complicated setting makes supervised learning and accurate kernel estimation impossible. To solve this problem, we resort to unsupervised learning without paired data, inspired by the recent successful image-toimage translation applications. With generative adversarial networks (GAN) as the basic component, we propose a Cycle-in-Cycle network structure to tackle the problem within three steps. First, the noisy and blurry input is mapped to a noise-free low-resolution space. Then the intermediate image is up-sampled with a pre-trained deep model. Finally, we fine-tune the two modules in an end-toend manner to get the high-resolution output. Experiments on NTIRE2018 datasets demonstrate that the proposed unsupervised method achieves comparable results as the stateof-the-art supervised models. * Yuan Yuan and Siyuan Liu are co-first authors. This work was done when they were interns at Sensetime. Contacting email: yuanyuan@szu.edu.cn Ground Truth Bicubic EDSR [17] BM3D+EDSR CinCGAN PSNR/SSIM 29.42/0.82 28.95/0.76 30.94/0.91 31.01/0.92 problem often have the following properties: 1) HR datasets are unavailable, 2) downscaling method is unknown, 3) input LR images are noisy and blurry. This problem is extremely difficult if the input images suffer from different kinds of degradation. For an easier case, in this study, we assume that input images are degraded with the same processing which is complex and unavailable. Under the above circumstances, models learned from synthetic data tend to generate similar results as traditional
Inspired by these methods, Yuan et al. REF propose a CinCGAN network, which learns to map the noise and blurry input LR image to a clean LR image space with cycle consistency losses.
Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Computer Science