Abstract
The total variation (TV) regularization method is an effective method for image deblurring in preserving edges. However, the TV based solutions usually have some staircase effects. In order to alleviate the staircase effects, we propose a new model for restoring blurred images under impulse noise. The model consists of an ℓ1-fidelity term and a TV with overlapping group sparsity (OGS) regularization term. Moreover, we impose a box constraint to the proposed model for getting more accurate solutions. The solving algorithm for our model is under the framework of the alternating direction method of multipliers (ADMM). We use an inner loop which is nested inside the majorization minimization (MM) iteration for the subproblem of the proposed method. Compared with other TV-based methods, numerical results illustrate that the proposed method can significantly improve the restoration quality, both in terms of peak signal-to-noise ratio (PSNR) and relative error (ReE).
Highlights
Image deblurring and denoising problems have been widely studied in the past decades
In this paper, inspired by the works from [40] and [41], we propose a new model for images deblurring under impulse noise by setting ψ in (4) to be the overlapping group sparsity (OGS)-total variation (TV) functional
We study a new regularization model by applying TV with OGS in the classic ‘1TV model for the image deblurring under impulse noise
Summary
Image deblurring and denoising problems have been widely studied in the past decades. It is widely assumed that observed images are the convolution of standard linear and space invariant blurring functions with true images plus some noise. Image deblurring and denoising is a typically illposed problem [1, 2]. To handle this problem, regularization technique is usually considered to obtain a stable and accurate solution. Regularization technique is usually considered to obtain a stable and accurate solution In this way, we need to solve the following problem min cðf Þ þ m Z jh ? We need to solve the following problem min cðf Þ þ m Z jh ? f À gj2dx; ð1Þ f
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
