Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
This repository is the official PyTorch implementation of Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
(arxiv, supplementary).
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Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially invariant across the whole image. However, such an assumption is rarely applicable for real images whose blur kernels are usually spatially variant due to factors such as object motion and out-of-focus. Hence, existing blind SR methods would inevitably give rise to poor performance in real applications. To address this issue, this paper proposes a mutual affine network (MANet) for spatially variant kernel estimation. Specifically, MANet has two distinctive features. First, it has a moderate receptive field so as to keep the locality of degradation. Second, it involves a new mutual affine convolution