  Blind image super-resolution (Blind-SR) aims to recover a high-resolution
(HR) image from its corresponding low-resolution (LR) input image with unknown
degradations. Most of the existing works design an explicit degradation
estimator for each degradation to guide SR. However, it is infeasible to
provide concrete labels of multiple degradation combinations (e.g., blur,
noise, jpeg compression) to supervise the degradation estimator training. In
addition, these special designs for certain degradation, such as blur, impedes
the models from being generalized to handle different degradations. To this
end, it is necessary to design an implicit degradation estimator that can
extract discriminative degradation representation for all degradations without
relying on the supervision of degradation ground-truth. In this paper, we
propose a Knowledge Distillation based Blind-SR network (KDSR). It consists of
a knowledge distillation based implicit degradation estimator network (KD-IDE)
and an efficient SR network. To learn the KDSR model, we first train a teacher
network: KD-IDE$_{T}$. It takes paired HR and LR patches as inputs and is
optimized with the SR network jointly. Then, we further train a student network
KD-IDE$_{S}$, which only takes LR images as input and learns to extract the
same implicit degradation representation (IDR) as KD-IDE$_{T}$. In addition, to
fully use extracted IDR, we design a simple, strong, and efficient IDR based
dynamic convolution residual block (IDR-DCRB) to build an SR network. We
conduct extensive experiments under classic and real-world degradation
settings. The results show that KDSR achieves SOTA performance and can
generalize to various degradation processes. The source codes and pre-trained
models will be released.
