Abstract
We propose a new fractional-order (space and time) total variation regularized model for multiplicative noise removal in this research article. We use the regularly varying fuzzy membership degrees to characterize the likelihood of a pixel related to edges, texture regions, and flat regions to improve model efficiency. This approach is capable of maintaining edges, textures, and other image information while significantly reducing the blocky effect. We opt for the option of local actions. In order to efficiently find the minimizer of the prescribed energy function, the semi-implicit gradient descent approach is used (which derives the corresponding fractional-order Euler-Lagrange equations). The existence and uniqueness of a solution to the suggested variational model are proved. Experimental results show the efficiency of the suggested model in visual enhancement, preserving details and reducing the blocky effect while extracting noise as well as an increase in the PSNR (dB), SSIM, relative error, and less CPU time(s) comparing to other schemes.
Highlights
I mage restoration is an inverse problem that has been extensively explored in the fields of image processing and computer vision
The goal of this paper is to propose a new fractional-order total variation regularized model for removing multiplicative noise and a fast methodology for achieving a numerical solution
EXPERIMENTAL RESULTS AND ANALYSIS Some image restoration outcomes are given in this part of the paper to confirm the achievement of the suggested model M1 and a fast algorithm for its numerical solution
Summary
I mage restoration is an inverse problem that has been extensively explored in the fields of image processing and computer vision. A real captured image may be degraded by some unavoidable random elements. These undesirable factors are known as noise and can be of various nature. The primary objective of the de-noise is to extract these unexpected elements from the image. In this way, the method of approximating the undiscovered image of interest from the degraded image given is recognized as restoring the image and has many applications in various fields.
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