Multiframe super‐resolution based on a high‐order spatially weighted regularisation

Laghrib, Amine; Alahyane, Mohamed; Ghazdali, Abdelghani; Hakim, Abdelilah; Raghay, Said

doi:10.1049/iet-ipr.2017.1046

Cited by 4 publications

(4 citation statements)

References 57 publications

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“…One approach to address the challenge is called MSR, which uses multiple frames of degraded images to reconstruct a high-resolution image [29][30][31]. Laghrib et al applied a non-local form of the bilateral total variation that takes into consideration complex spatial interactions within images [26]. The authors' approach, which builds on Bregman distances, avoids contrast loss and generates images with stronger edges and contours.…”

Section: Multiframe Super-resolution (Msr)mentioning

confidence: 99%

“…Laghrib et al . applied a non‐local form of the bilateral total variation that takes into consideration complex spatial interactions within images [26]. The authors’ approach, which builds on Bregman distances, avoids contrast loss and generates images with stronger edges and contours.…”

Section: Augmenting Pvr Systemsmentioning

confidence: 99%

“…Our approach integrates the multiframe super-resolution (MSR) model [21][22][23][24], which embeds a novel diffusion-steered regularisation functional into the existing PVR framework. The justification for using MSR techniques to minimise costs of PVR systems lands on the ability of such techniques to reconstruct high-quality images from their degraded versions [25,26]. MSR, in addition, helps to suppress noise, enhance resolution, and eliminate blur from the acquired images.…”

Section: Introductionmentioning

confidence: 99%

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Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

et al. 2020

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Palm vein recognition (PVR) refers to the contactless biometric identification method that uses palm vein patterns to confirm the identity of a person. Compared with other methods, PVR has received a wide attention because it provides more secure results. The veins, being located inside the human body, make PVR robust against tempering and changes in morphology of body features. Most PVR systems integrate four stages: image acquisition, pre-processing, feature extraction, and decision. The first two stages determine accuracy of the final identification results. Focusing on the pre-processing component, we discovered that the available approaches fail to generate more informative vein patterns by simultaneously suppressing noise and blur, and also by recovering semantically useful features (edges, contours, and lines) from the acquired images. This weakness calls for sophisticated acquisition devices that make PVR systems costly. In this work, we have proposed multiframe super-resolution (MSR) as a pre-processing stage to improve performance of the traditional PVR systems. MSR exploits information from multiple images of the same scene to reconstruct a high-resolution image. This technique signals the possibility of using inexpensive low-resolution imaging devices demanded by the traditional PVR systems. Experiments show that our method outperforms most classical methods.

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Section: Multiframe Super-resolution (Msr)mentioning

confidence: 99%

Section: Augmenting Pvr Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

et al. 2020

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“…Image registration is a challenging problem in image processing, its main object being to find geometrical correspondences between two images or more, which we call the reference and the template . It is an important tool in various applications such as super‐resolution, astronomy, robotics, and especially in biomedical imaging . The nonparametric image registration was treated by numerous approaches, including the ones based on regularization criterion .…”

Section: Introductionmentioning

confidence: 99%

A fast approach of nonparametric elastic image registration problem

Alahyane

Hakim

Laghrib

et al. 2019

Math Methods in App Sciences

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In this paper, we present a fast algorithm of the nonparametric elastic image registration using a simple implementation of the Range Restricted GMRES (RRGMRES) method. This approach differs from the others in the fact that it is specified to the tridiagonal block matrix type to resolve a nonsymmetric linear system. In what follows, we prove existence and uniqueness of minimizer of the elastic registration problem and present the corresponding discrete problem by employing a finite difference scheme. The accuracy of the proposed method is demonstrated on different image registration examples; we also show the speedup of the proposed approach by calculating the corresponding CPU time and compared it with the classical elastic registration method.

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A denoising model based on the fractional Beltrami regularization and its numerical solution

Ben‐loghfyry

Hakim

Laghrib

2022

J. Appl. Math. Comput.

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Multiframe super‐resolution based on a high‐order spatially weighted regularisation

Cited by 4 publications

References 57 publications

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

A fast approach of nonparametric elastic image registration problem

A denoising model based on the fractional Beltrami regularization and its numerical solution

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