Abstract
Multiplicative noise removal is of momentous significance in coherent imaging systems and various image processing applications. This paper proposes a new nonconvex variational model for multiplicative noise removal under the Weberized total variation (TV) regularization framework. Then, we propose and investigate another surrogate strictly convex objective function for Weberized TV regularization-based multiplicative noise removal model. Finally, we propose and design a novel way of fast alternating optimizing algorithm which contains three subminimizing parts and each of them permits a closed-form solution. Our experimental results show that our algorithm is effective and efficient to filter out multiplicative noise while well preserving the feature details.
Highlights
Image denoising is one of the fundamental problems in image processing and computer vision
Multiplicative noise appears in various image processing applications, for example, in synthetic aperture radar (SAR), ultrasound imaging, single particle emission computed tomography (SPECT), and positron emission tomography (PET) [1]
The main aim of this paper is to propose and investigate another nonconvex variational model for multiplicative noise removal which inspiring from the Weberized total variation (TV) regularization method [23, 24]
Summary
Image denoising is one of the fundamental problems in image processing and computer vision. One of the challenges in image denoising is its ill-posed nature To cope with this problem, a large number of approaches have been proposed, most of them under the regularization or the Bayesian frameworks [2, 3]. These approaches are supported on some form of a priori knowledge or regularization about the original image to be estimated Some of these methods, including Markov random field priors, wavelet-based priors or regularization [4, 5], curvelet-based diffusion [6], and total variation (TV) regularization [7,8,9] are considered the state-of-the-art. Variational functional regularization and partial differential equations(PDE-) based models have become international popular issues These models provide a good theoretical foundation to the image denoising task and other inverse problems such as image segmentation and image inpainting, and so forth. We refer the readers to [2, 3] and references for an overview of the subject [9]
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