2 research outputs found
Efficient Blind Deblurring under High Noise Levels
The goal of blind image deblurring is to recover a sharp image from a motion
blurred one without knowing the camera motion. Current state-of-the-art methods
have a remarkably good performance on images with no noise or very low noise
levels. However, the noiseless assumption is not realistic considering that low
light conditions are the main reason for the presence of motion blur due to
requiring longer exposure times. In fact, motion blur and high to moderate
noise often appear together. Most works approach this problem by first
estimating the blur kernel and then deconvolving the noisy blurred image.
In this work, we first show that current state-of-the-art kernel estimation
methods based on the gradient prior can be adapted to handle high
noise levels while keeping their efficiency. Then, we show that a fast
non-blind deconvolution method can be significantly improved by first denoising
the blurry image. The proposed approach yields results that are equivalent to
those obtained with much more computationally demanding methods
Handling noise in image deblurring via joint learning
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