Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution. (arXiv:2201.10747v1 [eess.IV])

Web: http://arxiv.org/abs/2201.10747

Unsupervised real world super resolution (USR) aims at restoring
high-resolution (HR) images given low-resolution (LR) inputs when paired data
is unavailable. One of the most common approaches is synthesizing noisy LR
images using GANs and utilizing a synthetic dataset to train the model in a
supervised manner. The goal of modeling the degradation generator is to
approximate the distribution of LR images given a HR image. Previous works
simply assumed the conditional distribution as a delta function and learned the …

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