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Most single image super-resolution (SR) methods are developed on syntheticlow-resolution (LR) and high-resolution (HR) image pairs, which are simulatedby a predetermined degradation operation, e.g., bicubic downsampling. However,these methods only learn the inverse process of the predetermined operation, sothey fail to super resolve the real-world LR images; the true formulationdeviates from the predetermined operation. To address this problem, we proposea novel supervised method to simulate an unknown degradation process with theinclusion of the prior hardware knowledge of the imaging system. We design anadaptive blurring layer (ABL) in the supervised learning framework to estimatethe target LR images. The hyperparameters of the ABL can be adjusted fordifferent imaging hardware. The experiments on the real-world datasets validatethat our degradation model can estimate LR images more accurately than thepredetermined degradation operation, as well as facilitate existing SR methodsto perform reconstructions on real-world LR images more accurately than theconventional approaches.

Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models