No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics
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 m...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/87058 http://hdl.handle.net/10220/44298 |
| _version_ | 1811683777724809216 |
|---|---|
| author | Li, Leida Yan, Ya Lu, Zhaolin Wu, Jinjian Gu, Ke Wang, Shiqi |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Li, Leida Yan, Ya Lu, Zhaolin Wu, Jinjian Gu, Ke Wang, Shiqi |
| author_sort | Li, Leida |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:18:08Z |
| format | Journal Article |
| id | ntu-10356/87058 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:18:08Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/870582020-03-07T11:48:51Z No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics Li, Leida Yan, Ya Lu, Zhaolin Wu, Jinjian Gu, Ke Wang, Shiqi School of Computer Science and Engineering School of Electrical and Electronic Engineering Image Quality Assessment Defocus Deblurring 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. Published version 2018-01-10T04:24:53Z 2019-12-06T16:34:13Z 2018-01-10T04:24:53Z 2019-12-06T16:34:13Z 2017 Journal Article 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. https://hdl.handle.net/10356/87058 http://hdl.handle.net/10220/44298 10.1109/ACCESS.2017.2661858 en IEEE Access © 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 9 p. application/pdf |
| spellingShingle | Image Quality Assessment Defocus Deblurring Li, Leida Yan, Ya Lu, Zhaolin Wu, Jinjian Gu, Ke Wang, Shiqi No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics |
| title | No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics |
| title_full | No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics |
| title_fullStr | No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics |
| title_full_unstemmed | No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics |
| title_short | No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics |
| title_sort | no reference quality assessment of deblurred images based on natural scene statistics |
| topic | Image Quality Assessment Defocus Deblurring |
| url | https://hdl.handle.net/10356/87058 http://hdl.handle.net/10220/44298 |
| work_keys_str_mv | AT lileida noreferencequalityassessmentofdeblurredimagesbasedonnaturalscenestatistics AT yanya noreferencequalityassessmentofdeblurredimagesbasedonnaturalscenestatistics AT luzhaolin noreferencequalityassessmentofdeblurredimagesbasedonnaturalscenestatistics AT wujinjian noreferencequalityassessmentofdeblurredimagesbasedonnaturalscenestatistics AT guke noreferencequalityassessmentofdeblurredimagesbasedonnaturalscenestatistics AT wangshiqi noreferencequalityassessmentofdeblurredimagesbasedonnaturalscenestatistics |