Deep Image and Kernel Prior Learning for Blind Super-Resolution

Recently, single image super-resolution (SR) has witnessed significant progress due to the powerful modeling capability of the deep learning networks. However, conventional deep learning-based super-resolution methods predict high-resolution (HR) images under the assumption of ideal degradation model such as the simulated bicubic down-sampling, and then unavoidably deteriorate the SR performance under un-controlled imaging conditions, such as real-world LR images. This study proposes an universal blind SR framework for adaptively and simultaneously predicting the underlying HR image and the counterpart blurring kernel from the observed LR image only. Specifically, we employ an encoder-decoder-based generative network to learn the inherent statistic prior of the HR image from a noise input while adopt a shallow convolution subnet with several stacked layers to estimate the blurring kernel from the observed LR image. Then, a convolution-based degradation module by setting the estimated blurring kernel as its weights is incorporated to obtain the approximated version of the LR image for formulating the loss function. In addition, a pre-trained discriminator is adopted to integrate the perceptual loss for recovering more accurate and natural HR image. We demonstrate the effectiveness of the proposed deep image and kernel prior learning framework using extensive experiments on both synthetic and real images, showing superiority over the state-of-the-art blind SR performance.