Blind Super-Resolution Kernel Estimation using an Internal-GAN

Super resolution (SR) methods typically assume that the low-resolution (LR) image was downscaled from the unknown high-resolution (HR) image by a fixed ideal' downscaling kernel (e.g. However, this is rarely the case in real LR images, in contrast to synthetically generated SR datasets. When the assumed downscaling kernel deviates from the true one, the performance of SR methods significantly deteriorates. This gave rise to Blind-SR - namely, SR when the downscaling kernel ( SR-kernel'') is unknown. It was further shown that the true SR-kernel is the one that maximizes the recurrence of patches across scales of the LR image.