  In various learning-based image restoration tasks, such as image denoising
and image super-resolution, the degradation representations were widely used to
model the degradation process and handle complicated degradation patterns.
However, they are less explored in learning-based image deblurring as blur
kernel estimation cannot perform well in real-world challenging cases. We argue
that it is particularly necessary for image deblurring to model degradation
representations since blurry patterns typically show much larger variations
than noisy patterns or high-frequency textures.In this paper, we propose a
framework to learn spatially adaptive degradation representations of blurry
images. A novel joint image reblurring and deblurring learning process is
presented to improve the expressiveness of degradation representations. To make
learned degradation representations effective in reblurring and deblurring, we
propose a Multi-Scale Degradation Injection Network (MSDI-Net) to integrate
them into the neural networks. With the integration, MSDI-Net can handle
various and complicated blurry patterns adaptively. Experiments on the GoPro
and RealBlur datasets demonstrate that our proposed deblurring framework with
the learned degradation representations outperforms state-of-the-art methods
with appealing improvements. The code is released at
https://github.com/dasongli1/Learning_degradation.
