Transitive Learning: Exploring the Transitivity of Degradations for Blind Super-Resolution

• URL: http://arxiv.org/abs/2103.15290v1
• Date: Mon, 29 Mar 2021 02:51:09 GMT
• Title: Transitive Learning: Exploring the Transitivity of Degradations for Blind Super-Resolution
• Authors: Yuanfei Huang, Jie Li, Yanting Hu, Xinbo Gao, Wen Lu
• Abstract summary: We propose a novel Transitive Learning method for blind Super-Resolution on transitive degradations (TLSR) We analyze and demonstrate the transitivity of degradations, including the widely used additive and convolutive degradations. We show that the proposed TLSR achieves superior performance and consumes less time against the state-of-the-art blind SR methods.
• Score: 89.4784684863403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Being extremely dependent on the iterative estimation and correction of data or models, the existing blind super-resolution (SR) methods are generally time-consuming and less effective. To address it, this paper proposes a transitive learning method for blind SR using an end-to-end network without any additional iterations in inference. To begin with, we analyze and demonstrate the transitivity of degradations, including the widely used additive and convolutive degradations. We then propose a novel Transitive Learning method for blind Super-Resolution on transitive degradations (TLSR), by adaptively inferring a transitive transformation function to solve the unknown degradations without any iterative operations in inference. Specifically, the end-to-end TLSR network consists of a degree of transitivity (DoT) estimation network, a homogeneous feature extraction network, and a transitive learning module. Quantitative and qualitative evaluations on blind SR tasks demonstrate that the proposed TLSR achieves superior performance and consumes less time against the state-of-the-art blind SR methods. The code is available at https://github.com/YuanfeiHuang/TLSR.

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