Efficient blind deblurring under high noise levels

Abstract

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 k and then deconvolving the noisy blurred image. In this work, we first show that current state-of-theart kernel estimation methods based on the 0 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.

Multiframe Correction Blind Deconvolution for Solar Image Restoration

“…Then, we use the efficient solver based on the HQS proposed by Anger et al (2019) to solve Equation (17):…”

“…Pan et al (2013Pan et al ( , 2014 solved kernel subproblems in the gradient domain of the image and showed its accuracy. Anger et al (2019) applied the L 2 norm of the gradient of the kernel and the L 1 norm of the kernel as constraints to get the blur kernel at a high noise level and showed good effectiveness and robustness. Javaran et al (2019) proposed an effective L 0.8 norm blur kernel prior to estimate the blur kernel in the gradient domain of the image, and the final estimated kernel converges to a compact structure.…”

Fast and Accurate Multi-Frame Super-Resolution of Satellite Images

“…We set λ1 = 0.4 and λ2 = 10 −2 . This inverse problem can be solved efficiently using a half-quadratic splitting method (Krishnan, Fergus, 2009), and extended to noisy image deblurring using (Anger et al, 2019). Figure 4 illustrates the effect of sharpening on real data.…”