  Currently, many blind deblurring methods assume blurred images are noise-free
and perform unsatisfactorily on the blurry images with noise. Unfortunately,
noise is quite common in real scenes. A straightforward solution is to denoise
images before deblurring them. However, even state-of-the-art denoisers cannot
guarantee to remove noise entirely. Slight residual noise in the denoised
images could cause significant artifacts in the deblurring stage. To tackle
this problem, we propose a cascaded framework consisting of a denoiser
subnetwork and a deblurring subnetwork. In contrast to previous methods, we
train the two subnetworks jointly. Joint learning reduces the effect of the
residual noise after denoising on deblurring, hence improves the robustness of
deblurring to heavy noise. Moreover, our method is also helpful for blur kernel
estimation. Experiments on the CelebA dataset and the GOPRO dataset show that
our method performs favorably against several state-of-the-art methods.
