Total Variation Filter via Multiquadric Radial Basis Function Approximation Scheme for Additive Noise Removal

• Published in: IEEE access Vol. 8; pp. 88241 - 88258
• Main Authors: Khan, Mushtaq Ahmad, Altamimi, Ahmed B., Khan, Zawar Hussain, Khattak, Khurram Shehzad, Ali, Murtaza, Ullah, Asmat, Khan, Sheraz, Khan, Muhammad Sohail, Abrar, Muhammad Faisal
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive noise; Algorithms; Data points; digital total variation (DTV) filter; Digital TV; Image denoising; Image edge detection; Image restoration; Mathematical analysis; Mathematical model; Meshless methods; meshless scheme; Minimization; multiquadric radial basis function (MQ-RBF); Noise; Noise reduction; Post-processing; Radial basis function; Regularization; restoration equation; Signal to noise ratio; traditional scheme; Visual effects; Visual signals
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2993322

The Digital Total Variation (DTV) scheme is a digitized energy regularization scheme used for image denoising. This technique takes advantage of being applied to arbitrarily located data points and also has the edge detective property. This article aims to introduce a novel meshless scheme using DTV filtering and Radial Basis Functions (RBFs) to solve the associated equation with the DTV model numerically which results in the image denoising to remove additive noise from image information. This meshless algorithm based on local collocation and Multiquadric Radial Basis Function. These appearances allow this algorithm not only to remove the additive noise from images but also to resolve the discontinuities sharply. It is also noticed that the proposed meshless scheme is simple, fast, computationally effective, requires simply post-processing, and can be easily implemented mathematically. Experimental results confirm that the peak signal-to-noise ratio, the structural similarity, signal-to-noise ratio, the visual effect, and the computational performance of this new meshless scheme are improved compared with state-of-the-art denoising schemes. Furthermore, the proposed scheme can be applied to colour images as well.