Unpaired Image Super-Resolution using Pseudo-Supervision

• URL: http://arxiv.org/abs/2002.11397v1
• Date: Wed, 26 Feb 2020 10:30:52 GMT
• Title: Unpaired Image Super-Resolution using Pseudo-Supervision
• Authors: Shunta Maeda
• Abstract summary: We propose an unpaired image super-resolution (SR) method using a generative adversarial network. Our network consists of an unpaired kernel/noise correction network and a pseudo-paired SR network. Experiments on diverse datasets show that the proposed method is superior to existing solutions to the unpaired SR problem.
• Score: 12.18340575383456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In most studies on learning-based image super-resolution (SR), the paired training dataset is created by downscaling high-resolution (HR) images with a predetermined operation (e.g., bicubic). However, these methods fail to super-resolve real-world low-resolution (LR) images, for which the degradation process is much more complicated and unknown. In this paper, we propose an unpaired SR method using a generative adversarial network that does not require a paired/aligned training dataset. Our network consists of an unpaired kernel/noise correction network and a pseudo-paired SR network. The correction network removes noise and adjusts the kernel of the inputted LR image; then, the corrected clean LR image is upscaled by the SR network. In the training phase, the correction network also produces a pseudo-clean LR image from the inputted HR image, and then a mapping from the pseudo-clean LR image to the inputted HR image is learned by the SR network in a paired manner. Because our SR network is independent of the correction network, well-studied existing network architectures and pixel-wise loss functions can be integrated with the proposed framework. Experiments on diverse datasets show that the proposed method is superior to existing solutions to the unpaired SR problem.

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