Fatma Faek, O. Nomir, Ehab Essa, Ebrahim El-henawy
{"title":"基于小波的对比度限制自适应直方图均衡化视频增强","authors":"Fatma Faek, O. Nomir, Ehab Essa, Ebrahim El-henawy","doi":"10.21608/mjcis.2018.311997","DOIUrl":null,"url":null,"abstract":"A combination of wavelet transforms and contrast limited adaptive histogram equalization (CLAHE) techniques are used to efficiently enhance videos. The proposed technique handles the noises within video frames and enhances the resolution of the video. Lifting wavelet transform (LWT) and stationary wavelet transform (SWT) are applied to separate original frame into the low-frequency sub-bands, and the high-frequency sub-bands. Thereafter, we applied the interpolation to correct the coefficients of the high-frequency and the original frame separately. Next, Inverse Lifting wavelet transform (ILWT) is utilized for the integration of each all these enhanced sub-band. Finally, the CLAHE algorithm is applied to make the details of the frame more visible, and meaningful to generally improve the resolution of the video. The output video shows that the proposed technique enhances the quality of resolution videos under various environmental conditions, alleviates noises and avoids the over-enhancement problems.","PeriodicalId":253950,"journal":{"name":"Mansoura Journal for Computer and Information Sciences","volume":"290 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Wavelet-based Video Enhancement Using Contrast Limited Adaptive Histogram Equalization\",\"authors\":\"Fatma Faek, O. Nomir, Ehab Essa, Ebrahim El-henawy\",\"doi\":\"10.21608/mjcis.2018.311997\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A combination of wavelet transforms and contrast limited adaptive histogram equalization (CLAHE) techniques are used to efficiently enhance videos. The proposed technique handles the noises within video frames and enhances the resolution of the video. Lifting wavelet transform (LWT) and stationary wavelet transform (SWT) are applied to separate original frame into the low-frequency sub-bands, and the high-frequency sub-bands. Thereafter, we applied the interpolation to correct the coefficients of the high-frequency and the original frame separately. Next, Inverse Lifting wavelet transform (ILWT) is utilized for the integration of each all these enhanced sub-band. Finally, the CLAHE algorithm is applied to make the details of the frame more visible, and meaningful to generally improve the resolution of the video. The output video shows that the proposed technique enhances the quality of resolution videos under various environmental conditions, alleviates noises and avoids the over-enhancement problems.\",\"PeriodicalId\":253950,\"journal\":{\"name\":\"Mansoura Journal for Computer and Information Sciences\",\"volume\":\"290 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mansoura Journal for Computer and Information Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21608/mjcis.2018.311997\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mansoura Journal for Computer and Information Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21608/mjcis.2018.311997","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wavelet-based Video Enhancement Using Contrast Limited Adaptive Histogram Equalization
A combination of wavelet transforms and contrast limited adaptive histogram equalization (CLAHE) techniques are used to efficiently enhance videos. The proposed technique handles the noises within video frames and enhances the resolution of the video. Lifting wavelet transform (LWT) and stationary wavelet transform (SWT) are applied to separate original frame into the low-frequency sub-bands, and the high-frequency sub-bands. Thereafter, we applied the interpolation to correct the coefficients of the high-frequency and the original frame separately. Next, Inverse Lifting wavelet transform (ILWT) is utilized for the integration of each all these enhanced sub-band. Finally, the CLAHE algorithm is applied to make the details of the frame more visible, and meaningful to generally improve the resolution of the video. The output video shows that the proposed technique enhances the quality of resolution videos under various environmental conditions, alleviates noises and avoids the over-enhancement problems.