  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.
