Total variable-order variation as a regularizer applied on multi-frame image super-resolution

Ben‐loghfyry, Anouar; Hakim, Abdelilah

doi:10.1007/s00371-023-02996-7

Cited by 7 publications

(1 citation statement)

References 42 publications

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“…Image processing is a method that aims to improve the image quality, to get an enhanced image or to extract some useful information from it, in order to adjust the obtained image for further processing or analysis. Image processing has many domains such as image denoising [1–5] (which is the main work in our paper), image super‐resolution [6–8], image recognition [9], image encryption [10, 11], image reconstruction [12]. For image denoising, it can be reformulated mathematically, by finding an approximation of

{u}_0&amp;amp;#x0003D;u&amp;amp;#x0002B;\mu,

where

u

is the original image from a degraded or noisy image

u

, and

\mu

is the Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

Anisotropic equation based on fractional diffusion tensor for image noise removal

Charkaoui,

Ben‐loghfyry

2024

Math Methods in App Sciences

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In this paper, we propose an anisotropic diffusion equation based on fractional‐order tensor diffusion applied on noisy images. We start by investigating the existence and uniqueness results of the proposed model. We also provide a discretization scheme based on the finite difference method. In order to prove the efficacy of our proposed approach, we deliver some simulations, which show robustness and remarkable results against noise while preserving edges and corners, compared to some well‐known models in literature. For a fair comparison, we manage to obtain the optimal parameters of every model thanks to PSNR and SSIM. We demonstrate the robustness efficacy of our model against high level of noise.

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