  In recent years, deep neural network-based restoration methods have achieved
state-of-the-art results in various image deblurring tasks. However, one major
drawback of deep learning-based deblurring networks is that large amounts of
blurry-clean image pairs are required for training to achieve good performance.
Moreover, deep networks often fail to perform well when the blurry images and
the blur kernels during testing are very different from the ones used during
training. This happens mainly because of the overfitting of the network
parameters on the training data. In this work, we present a method that
addresses these issues. We view the non-blind image deblurring problem as a
denoising problem. To do so, we perform Wiener filtering on a pair of blurry
images with the corresponding blur kernels. This results in a pair of images
with colored noise. Hence, the deblurring problem is translated into a
denoising problem. We then solve the denoising problem without using explicit
clean target images. Extensive experiments are conducted to show that our
method achieves results that are on par to the state-of-the-art non-blind
deblurring works.

Computer Vision and Pattern Recognition;Image and Video Processing