Abstract
Blind image deblurring is a long-standing challenging problem to improve the sharpness of an image as a prerequisite step. Many iterative methods are widely used for the deblurring image, but care must be taken to ensure that the methods have fast convergence and accuracy solutions. To address these problems, we propose a gradient-wise step size search strategy for iterative methods to achieve robustness and accelerate the deblurring process. We further modify the conjugate gradient method with the proposed strategy to solve the bling image deblurring problem. The gradient-wise step size aims to update gradient for each pixel individually, instead of updating step size by the fixed factor. The modified conjugate gradient method improves the convergence performance computation speed with a gradient-wise step size. Experimental results show that our method effectively estimates the sharp image for both motion blur images and defocused images. The results of synthetic datasets and natural images are better than what is achieved with other state-of-the-art blind image deblurring methods.
Highlights
1.1 Retalted workIn many applications, ranging from computer vision and pattern recognition to machine intelligence, the demand for image deblurring is widely encountered as a prerequisite [1]– [3]
For nonblind image deblurring (NBID), it is to deblur an image with a known blurring kernel
We focus on iterative methods by designing a new strategy to improve the comprehensive effect of the BID process
Summary
1.1 Retalted workIn many applications, ranging from computer vision and pattern recognition to machine intelligence, the demand for image deblurring is widely encountered as a prerequisite [1]– [3]. The image deblurring problem can be classified into two categories: non-blind, in which the blur kernel is assumed to be known [5], and blind, in which the blur kernel is unknown [8]–[20]. In order to make the blind deblurring problem well-posed, image prior, and blur kernel model exploitation is the key to most effective methods. The blur kernel model includes the depth variation model [26], Forward motion model [27], non uniform model [28]. Most of these methods exploit the image prior and blur kernel model under the Bayesian framework [29]. Even though incorporating regularization to exploit image prior and kernel model helps to increase the probability for achieving a good local solution, the ideal deblurring result cannot be acquired
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