Diffusion‐steered super‐resolution method based on the Papoulis–Gerchberg algorithm

Maiseli, Baraka

doi:10.1049/iet-ipr.2015.0715

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…In this work, we have proposed an approach that may allow PVR systems to use low-cost acquisition sensors without compromising the accuracy of such systems. 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].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…In this approach, a new edge indicator based on the eigenvalues of the trilateral structure tensor to identify the local spatial property of each pixel was introduced and proved its efficiency in avoiding blur. An adaptive diffusionbased regulariser [44,45] was also presented to preserve important image features, but did not tackle the blurring effect. More recently, an adaptive new tensor-based diffusion term [46] was proposed which can preserve sharp edges and corners much better while avoiding artefacts.…”

Section: Introductionmentioning

confidence: 99%

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

Laghrib

Alahyane

Ghazdali³

et al. 2018

IET Image Processing

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High-quality AFM image acquisition of living cells by modified residual encoder-decoder network

Wang,

Yang,

Wang

et al. 2024

Journal of Structural Biology

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Diffusion‐steered super‐resolution method based on the Papoulis–Gerchberg algorithm

Cited by 3 publications

References 43 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

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

High-quality AFM image acquisition of living cells by modified residual encoder-decoder network

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