Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization

• URL: http://arxiv.org/abs/2004.00843v1
• Date: Thu, 2 Apr 2020 07:06:55 GMT
• Title: Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization
• Authors: Marija Vella and Jo\~ao F. C. Mota
• Abstract summary: Single-image super-resolution is the process of increasing the resolution of an image, obtaining a high-resolution (HR) image from a low-resolution (LR) one. By leveraging large training datasets, convolutional neural networks (CNNs) currently achieve the state-of-the-art performance in this task. We propose to post-process the CNN outputs with an optimization problem that we call TV-TV minimization, which enforces consistency.
• Score: 7.538482310185135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-image super-resolution is the process of increasing the resolution of an image, obtaining a high-resolution (HR) image from a low-resolution (LR) one. By leveraging large training datasets, convolutional neural networks (CNNs) currently achieve the state-of-the-art performance in this task. Yet, during testing/deployment, they fail to enforce consistency between the HR and LR images: if we downsample the output HR image, it never matches its LR input. Based on this observation, we propose to post-process the CNN outputs with an optimization problem that we call TV-TV minimization, which enforces consistency. As our extensive experiments show, such post-processing not only improves the quality of the images, in terms of PSNR and SSIM, but also makes the super-resolution task robust to operator mismatch, i.e., when the true downsampling operator is different from the one used to create the training dataset.

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