Restoration algorithm for noisy complex illumination
Although promising results have been achieved in the restoration of complex illumination images with the Retinex algorithm, there are still some drawbacks in the processing of Retinex. Considering the noise characteristics of complex illumination images, in this study, we propose a novel restoration...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-03-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5163 |
| _version_ | 1797684251883208704 |
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| author | Zhanwen Liu Tao Gao Fanjie Kong Ziheng Jiao Aodong Yang Shuying Li Bo Liu |
| author_facet | Zhanwen Liu Tao Gao Fanjie Kong Ziheng Jiao Aodong Yang Shuying Li Bo Liu |
| author_sort | Zhanwen Liu |
| collection | DOAJ |
| description | Although promising results have been achieved in the restoration of complex illumination images with the Retinex algorithm, there are still some drawbacks in the processing of Retinex. Considering the noise characteristics of complex illumination images, in this study, we propose a novel restoration algorithm for noisy complex illumination, which combines guided adaptive multi‐scale Retinex (GAMSR) and improvement BayesShrink threshold filtering (IBTF) based on double‐density dual‐tree complex wavelet transform (DDDTCWT) domain. Extensive restoration experiments are conducted on three typical types images and the same image with different noises. On the basis of a series of evaluation indexes, we compare our method to those of state‐of‐the‐art algorithms. The results show that (i) SSIM of the proposed IBTF is superior to traditional Bayes threshold method by 15% as the standard variance is 100. (ii) PSNR of the proposed GAMSR enhances 15% to traditional MSR. (iii) The clarity of final results for restoration speeds up three times than that of original images, and the information entropy is improved slightly too. Therefore, the proposed method can effectively enhance the details, edges and textures of the image under complex illumination and noises. |
| first_indexed | 2024-03-12T00:27:55Z |
| format | Article |
| id | doaj.art-2ac2f71cec76484bae3998ab1d757011 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:27:55Z |
| publishDate | 2019-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-2ac2f71cec76484bae3998ab1d7570112023-09-15T10:31:50ZengWileyIET Computer Vision1751-96321751-96402019-03-0113222423210.1049/iet-cvi.2018.5163Restoration algorithm for noisy complex illuminationZhanwen Liu0Tao Gao1Fanjie Kong2Ziheng Jiao3Aodong Yang4Shuying Li5Bo Liu6School of Information EngineeringChang'an UniversityXi'an710064ShaanxiPeople's Republic of ChinaSchool of Information EngineeringChang'an UniversityXi'an710064ShaanxiPeople's Republic of ChinaSchool of Information EngineeringChang'an UniversityXi'an710064ShaanxiPeople's Republic of ChinaSchool of Information EngineeringChang'an UniversityXi'an710064ShaanxiPeople's Republic of ChinaSchool of Information EngineeringChang'an UniversityXi'an710064ShaanxiPeople's Republic of ChinaSchool of AutomationXi'an University of Posts & TelecommunicationsXi'an710121ShaanxiPeople's Republic of ChinaDepartment of Computer Science and Software EngineeringAuburn UniversityAuburnState of Alabama, AL36849USAAlthough promising results have been achieved in the restoration of complex illumination images with the Retinex algorithm, there are still some drawbacks in the processing of Retinex. Considering the noise characteristics of complex illumination images, in this study, we propose a novel restoration algorithm for noisy complex illumination, which combines guided adaptive multi‐scale Retinex (GAMSR) and improvement BayesShrink threshold filtering (IBTF) based on double‐density dual‐tree complex wavelet transform (DDDTCWT) domain. Extensive restoration experiments are conducted on three typical types images and the same image with different noises. On the basis of a series of evaluation indexes, we compare our method to those of state‐of‐the‐art algorithms. The results show that (i) SSIM of the proposed IBTF is superior to traditional Bayes threshold method by 15% as the standard variance is 100. (ii) PSNR of the proposed GAMSR enhances 15% to traditional MSR. (iii) The clarity of final results for restoration speeds up three times than that of original images, and the information entropy is improved slightly too. Therefore, the proposed method can effectively enhance the details, edges and textures of the image under complex illumination and noises.https://doi.org/10.1049/iet-cvi.2018.5163noisy complex illuminationrestoration algorithmGAMSRIBTFhigh-frequency subbandlow-frequency subband |
| spellingShingle | Zhanwen Liu Tao Gao Fanjie Kong Ziheng Jiao Aodong Yang Shuying Li Bo Liu Restoration algorithm for noisy complex illumination IET Computer Vision noisy complex illumination restoration algorithm GAMSR IBTF high-frequency subband low-frequency subband |
| title | Restoration algorithm for noisy complex illumination |
| title_full | Restoration algorithm for noisy complex illumination |
| title_fullStr | Restoration algorithm for noisy complex illumination |
| title_full_unstemmed | Restoration algorithm for noisy complex illumination |
| title_short | Restoration algorithm for noisy complex illumination |
| title_sort | restoration algorithm for noisy complex illumination |
| topic | noisy complex illumination restoration algorithm GAMSR IBTF high-frequency subband low-frequency subband |
| url | https://doi.org/10.1049/iet-cvi.2018.5163 |
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