Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

• URL: http://arxiv.org/abs/2007.03053v2
• Date: Thu, 5 Nov 2020 18:25:16 GMT
• Title: Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
• Authors: Mohammad Saeed Rad, Thomas Yu, Claudiu Musat, Hazim Kemal Ekenel, Behzad Bozorgtabar, Jean-Philippe Thiran
• Abstract summary: We propose to handle real-world SR by splitting this ill-posed problem into two comparatively more well-posed steps. First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner. Second, we take a generic SR network trained on bicubically downsampled images to super-resolve the transformed LR image.
• Score: 22.339751911637077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling. However, in real-world SR, there is a large variety of realistic image degradations and analytically modeling these realistic degradations can prove quite difficult. In this work, we propose to handle real-world SR by splitting this ill-posed problem into two comparatively more well-posed steps. First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner, by using both real LR/HR pairs and synthetic pairs. Second, we take a generic SR network trained on bicubically downsampled images to super-resolve the transformed LR image. The first step of the pipeline addresses the problem by registering the large variety of degraded images to a common, well understood space of images. The second step then leverages the already impressive performance of SR on bicubically downsampled images, sidestepping the issues of end-to-end training on datasets with many different image degradations. We demonstrate the effectiveness of our proposed method by comparing it to recent methods in real-world SR and show that our proposed approach outperforms the state-of-the-art works in terms of both qualitative and quantitative results, as well as results of an extensive user study conducted on several real image datasets.

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