Abstract:Previous single-image super-resolution (SISR) methods assume that the blur kernel is known (e.g., bicubic) when degrading from high-resolution (HR) images to low-resolution (LR) images. They use a single degradation to train a model to restore HR images. However, the actual degradation in real-world is often unknown. It is difficult to deal with LR images caused by different degradations. To cope with the above situation, previous methods attempt to restore SR images using a blur kernel estimation structure th… Show more
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