Application of Wiener Filter Based on Improved BB Gradient Descent in Iris Image Restoration.
Iris recognition, renowned for its exceptional precision, has been extensively utilized across diverse industries. However, the presence of noise and blur frequently compromises the quality of iris images, thereby adversely affecting recognition accuracy. In this research, we have refined the tradit...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 1165 - 1184 |
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| Autores principales: | , |
| Formato: | algorithm pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081741&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081741 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081741 184081741 184081741 10.1007/s10278-024-01238-z 184081741 ppf: 1165 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Application of Wiener Filter Based on Improved BB Gradient Descent in Iris Image Restoration. aug: au: Qin, Chuandong Zhang, Yiqing affil: https://ror.org/05xjevr11 School of Mathematics and Information Science, North Minzu University, 750021, Yinchuan, China sug: subj: Iris Pathology Image Enhancement Artificial Intelligence Methods Algorithms Image Processing, Computer Assisted Methods Machine Learning Algorithms Signal Processing, Computer Assisted Sensitivity and Specificity ab: Iris recognition, renowned for its exceptional precision, has been extensively utilized across diverse industries. However, the presence of noise and blur frequently compromises the quality of iris images, thereby adversely affecting recognition accuracy. In this research, we have refined the traditional Wiener filter image restoration technique by integrating it with a gradient descent strategy, specifically employing the Barzilai-Borwein (BB) step size selection. This innovative approach is designed to enhance both the precision and resilience of iris recognition systems. The BB gradient method is adept at optimizing the parameters of the Wiener filter by introducing simulated blurring and noise conditions to the iris images. Through this process, it is capable of restoring images that have been degraded by blur and noise, leading to a significant improvement in the clarity of the restored images and, consequently, a notable elevation in recognition performance. The results of our experiments have demonstrated that this advanced method surpasses conventional filtering techniques in terms of both subjective visual quality assessments and objective peak signal-to-noise ratio (PSNR) evaluations. pubtype: Academic Journal doctype: algorithm pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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