Abstract
Image deblurring is a challenging illposed problem with widespread applications. Most existing deblurring methods make use of image priors or priors on the PSF to achieve accurate results. The performance of these methods depends on various factors such as the presence of well-lit conditions in the case of dark image priors and in case of statistical image priors the assumption the image follows a certain distribution might not be fully accurate. This holds for statistical priors used on the blur kernel as well. The aim of this paper is to propose a novel image deblurring method which can be readily extended to various applications such that it effectively deblurs the image irrespective of the various factors affecting its capture. A hybrid regularization method is proposed which uses a TV regularization framework with varying sparsity inducing priors. The edges of the image are accurately recovered due to the TV regularization. The sparsity prior is implemented through a dictionary such that varying weights of sparsity is induced based on the different image regions. This helps in smoothing the unwanted artifacts generated due to blur in the uniform regions of the image.
Highlights
The widespread applications of digital images such as in astronomical imaging, medical imaging law and order maintenance, remote sensing etc. has led to greater interest in improving the existing image deblurring methods
In order to facilitate adaptive regularization which varies with respect to the spatial regions of the image we introduce a regularization parameter γ
The proposed image deblurring model based on the Total Variation (TV) regularization framework, along with an adaptive sparsity prior is shown in the equation below
Summary
The widespread applications of digital images such as in astronomical imaging, medical imaging law and order maintenance, remote sensing etc. has led to greater interest in improving the existing image deblurring methods. Has led to greater interest in improving the existing image deblurring methods. Image deblurring is the process of recovering the true sharp image from a degraded image which is both blurry and noisy. Where g represents the captured degraded image, which is both blurry and noisy and u represents the true sharp image, both these images are represented through. Deblurring is the process of effectively recovering the blur function H and subsequently acquiring the true sharp image u from it. Blurring is inevitable in digital cameras and requires image deblurring techniques to restore the captured images which are vital in applications such as medical imaging, remote sensing, law and order maintenance through surveillance cameras etc
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