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No-reference image quality assessment for image auto-denoising / Xiangfei Kong in International journal of computer vision, vol 126 n° 5 (May 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 5 (May 2018) . - pp 537 - 549 Titre : No-reference image quality assessment for image auto-denoising Type de document : Article/Communication Auteurs : Xiangfei Kong, Auteur ; Qingxiong Yang, Auteur Année de publication : 2018 Article en page(s) : pp 537 - 549 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] appariement d'images [Termes IGN] filtrage du bruit [Termes IGN] plus proche voisin, algorithme du [Termes IGN] qualité d'image Résumé : (Auteur) This paper proposes two new non-reference image quality metrics that can be adopted by the state-of-the-art image/video denoising algorithms for auto-denoising. The first metric is proposed based on the assumption that the noise should be independent of the original image. A direct measurement of this dependence is, however, impractical due to the relatively low accuracy of existing denoising method. The proposed metric thus tackles the homogeneous regions and highly-structured regions separately. Nevertheless, this metric is only stable when the noise level is relatively low. Most denoising algorithms reduce noise by (weighted) averaging repeated noisy measurements. As a result, another metric is proposed for high-level noise based on the fact that more noisy measurements will be required when the noise level increases. The number of measurements before converging is thus related to the quality of noisy images. Our patch-matching based metric proposes to iteratively find and add noisy image measurements for averaging until there is no visible difference between two successively averaged images. Both metrics are evaluated on LIVE2 (Sheikh et al. in LIVE image quality assessment database release 2: 2013) and TID2013 (Ponomarenko et al. in Color image database tid2013: Peculiarities and preliminary results: 2005) data sets using standard Spearman and Kendall rank-order correlation coefficients (ROCC), showing that they subjectively outperforms current state-of-the-art no-reference metrics. Quantitative evaluation w.r.t. different level of synthetic noisy images also demonstrates consistently higher performance over state-of-the-art non-reference metrics when used for image denoising. Numéro de notice : A2018-409 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-1054-2 Date de publication en ligne : 17/11/2017 En ligne : https://doi.org/10.1007/s11263-017-1054-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90883 [article]