Transitive Learning: Exploring the Transitivity of Degradations for Blind Super Resolution
Being extremely dependent on the iterative estimation and correction of data or models, the existing blind superresolution (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 stateof the art blind SR methods. The code is available at github.com/YuanfeiHuang/TLSR.
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