Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87058
Title: No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics
Authors: Li, Leida
Yan, Ya
Lu, Zhaolin
Wu, Jinjian
Gu, Ke
Wang, Shiqi
Keywords: Image Quality Assessment
Defocus Deblurring
Issue Date: 2017
Source: Li, L., Yan, Y., Lu, Z., Wu, J., Gu, K., & Wang, S. (2017). No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics. IEEE Access, 5, 2163-2171.
Series/Report no.: IEEE Access
Abstract: Blurring is one of the most common distortions in digital images. In the past decade, extensive image deblurring algorithms have been proposed to restore a latent clean image from its blurred version. However, very little work has been dedicated to the quality assessment of deblurred images, which may hinder further development of more advanced deblurring techniques. Motivated by this, this paper presents a no-reference quality metric for defocus deblured images based on Natural Scene Statistics (NSS). Two categories of NSS features are extracted in both the spatial and frequency domains to account for both the global and local aspects of distortions in deblurred images. Specifically, the spatial domain NSS features are used to characterize the global naturalness, and the frequency domain NSS features are used to portray the local structural distortions. All features are combined to train a support vector regression model for quality prediction of defocus deblurred images. The performance of the proposed metric is evaluated in a subjectively rated defocus deblurred image database. The experimental results demonstrate the advantages of the proposed metric over the relevant state-of-the-arts. As an application, the proposed metric is further used for benchmarking deblurring algorithms and very encouraging results are achieved.
URI: https://hdl.handle.net/10356/87058
http://hdl.handle.net/10220/44298
DOI: http://dx.doi.org/10.1109/ACCESS.2017.2661858
Rights: © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
Appears in Collections:EEE Journal Articles
SCSE Journal Articles

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