Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution

Abstract

Blind super-resolution (blind-SR) is an important task in the field of computer vision and has various applications in real-world. Blur kernel estimation is the main element of blind-SR along with the adaptive SR networks and a more accurately estimated kernel guarantees a better performance. Recently, generative adversarial networks (GANs), comparing recurrence patches across scales, have been the most successful unsupervised kernel estimation methods. However, they still involve several problems. ① Their sharpness discrimination ability has been noted as being too weak, causing them to focus more on pattern shapes than sharpness. ② In some cases, kernel correction processes were omitted; however, these are essential because the optimally generated kernel may be narrower than a point spread function (PSF) except when the PSF is ideal low-pass filter. ③ Previous studies also did not consider that GANs are affected by the thickness of edges as well as PSF. Thus, in this paper, 1) we propose a degradation and ranking comparison process designed to induce GAN models to became sensitive to image sharpness, and 2) propose a scale-free kernel correction technique using Gaussian kernel approximation including a thickness parameter. To improve the kernel accuracy further, we 3) propose a combination model of the proposed GAN and DIP(deep image prior) for more supervision, and designed a kernel correction network to propagate gradients through developed correction method. Several experiments demonstrate that our methods enhanced the $l_{2}$ error and the shape of the kernel significantly. In addition, by combining with ordinary blind-SR algorithms, the best reconstruction accuracy was achieved among unsupervised blur kernel estimation methods.

Keywords

• Blind super-resolution
• kernel estimation
• kernel correction
• generative adversarial networks
• deep image prior