Efficient Test-Time Adaptation for Super-Resolution with Second-Order
  Degradation and Reconstruction
Zeshuai Deng Zhuokun Chen Shuaicheng Niu Thomas H. Li Bohan Zhuang Mingkui Tan
Image super-resolution (SR) aims to learn a mapping from low-resolution (LR)
to high-resolution (HR) using paired HR-LR training images. Conventional SR
methods typically gather the paired training data by synthesizing LR images
from HR images using a predetermined degradation model, e.g., Bicubic
down-sampling. However, the realistic degradation type of test images may
mismatch with the training-time degradation type due to the dynamic changes of
the real-world scenarios, resulting in inferior-quality SR images. To address
this, existing methods attempt to estimate the degradation model and train an
image-specific model, which, however, is quite time-consuming and impracticable
to handle rapidly changing domain shifts. Moreover, these methods largely
concentrate on the estimation of one degradation type (e.g., blur degradation),
overlooking other degradation types like noise and JPEG in real-world test-time
scenarios, thus limiting their practicality. To tackle these problems, we
present an efficient test-time adaptation framework for SR, named SRTTA, which
is able to quickly adapt SR models to test domains with different/unknown
degradation types. Specifically, we design a second-order degradation scheme to
construct paired data based on the degradation type of the test image, which is
predicted by a pre-trained degradation classifier. Then, we adapt the SR model
by implementing feature-level reconstruction learning from the initial test
image to its second-order degraded counterparts, which helps the SR model
generate plausible HR images. Extensive experiments are conducted on newly
synthesized corrupted DIV2K datasets with 8 different degradations and several
real-world datasets, demonstrating that our SRTTA framework achieves an
impressive improvement over existing methods with satisfying speed. The source
code is available at https://github.com/DengZeshuai/SRTTA.