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Being extremely dependent on iterative estimation of the degradation prior oroptimization of the model from scratch, the existing blind super-resolution(SR) methods are generally time-consuming and less effective, as the estimationof degradation proceeds from a blind initialization and lacks interpretabledegradation priors. To address it, this paper proposes a transitional learningmethod for blind SR using an end-to-end network without any additionaliterations in inference, and explores an effective representation for unknowndegradation. To begin with, we analyze and demonstrate the transitionality ofdegradations as interpretable prior information to indirectly infer the unknowndegradation model, including the widely used additive and convolutivedegradations. We then propose a novel Transitional Learning method for blindSuper-Resolution (TLSR), by adaptively inferring a transitional transformationfunction to solve the unknown degradations without any iterative operations ininference. Specifically, the end-to-end TLSR network consists of a degree oftransitionality (DoT) estimation network, a homogeneous feature extractionnetwork, and a transitional learning module. Quantitative and qualitativeevaluations on blind SR tasks demonstrate that the proposed TLSR achievessuperior performances and costs fewer complexities against the state-of-the-artblind SR methods. The code is available at github.com/YuanfeiHuang/TLSR.

Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution