SVDN: A spatially variant degradation network for blind image super-resolution

Blind super-resolution (SR) aims at generating a high-resolution image from a low-resolution image where the degradation kernels are unknown. Most existing blind super-resolution approaches apply an estimated blur kernel as a degradation prior to the entire image. However, due to the intrinsic degradation characteristics of camera lens or different depth-of-filed in the image, the degradation kernels in different regions are not exactly the same. To address this issue, we propose a spatially variant degradation network (SVDN) for blind SR with a pixel-wise kernel estimation block to handle the complex and unknown degradations in blind SR tasks. Moreover, to fully exploit the estimated degradation information, the output of network is influenced by the estimated kernel via an affine transformation applied to the feature maps in each middle layer. The proposed method permits a lot of freedom in adapting the image degradation locally. Extensive experiments on synthetic and real-world images demonstrate that the proposed method generates more accurate SR results under the complex degradation settings and provide naturally visual results both on the in-focus and out-of-focus regions of the images.