Handling noise in image deblurring via joint learning
This addresses a practical issue for computer vision applications where real-world images often contain noise, though it is an incremental improvement over existing deblurring techniques.
The paper tackles the problem of image deblurring in the presence of noise, which causes artifacts in existing methods, by proposing a cascaded framework with joint learning of denoising and deblurring subnetworks, resulting in improved robustness and performance on datasets like CelebA and GOPRO.
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.