A novel convolutional Atangana-Baleanu fractional derivative mask for medical image edge analysis

Appati, Justice Kwame; Owusu, Ebenezer; Soli, Michael Agbo Tettey; Adu-Manu, Kofi Sarpong

doi:10.1080/0952813x.2022.2108147

Cited by 2 publications

(1 citation statement)

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“…Their suggested nonlinear filter mask not only accentuates and maintains intricate features, but also proficiently mitigates noise within the image. Building upon the groundwork laid by FO differentiation, a selection of researchers introduced the concept of a nonlinear filter mask [21][22][23]. This groundbreaking development bolstered the retention of intricate image features while proficiently minimizing noise within the image, despite necessitating substantial computational resources.…”

Section: Introductionmentioning

confidence: 99%

Image Restoration with Fractional-Order Total Variation Regularization and Group Sparsity

2023

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In this paper, we present a novel image denoising algorithm, specifically designed to effectively restore both the edges and texture of images. This is achieved through the use of an innovative model known as the overlapping group sparse fractional-order total variation regularization model (OGS-FOTVR). The OGS-FOTVR model ingeniously combines the benefits of the fractional-order (FO) variation domain with an overlapping group sparsity measure, which acts as its regularization component. This is further enhanced by the inclusion of the well-established L2-norm, which serves as the fidelity term. To simplify the model, we employ the alternating direction method of multipliers (ADMM), which breaks down the model into a series of more manageable sub-problems. Each of these sub-problems can then be addressed individually. However, the sub-problem involving the overlapping group sparse FO regularization presents a high level of complexity. To address this, we construct an alternative function for this sub-problem, utilizing the mean inequality principle. Subsequently, we employ the majorize-minimization (MM) algorithm to solve it. Empirical results strongly support the effectiveness of the OGS-FOTVR model, demonstrating its ability to accurately recover texture and edge information in images. Notably, the model performs better than several advanced variational alternatives, as indicated by superior performance metrics across three image datasets, PSNR, and SSIM.

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Section: Introductionmentioning

confidence: 99%