{"title":"基于小波分解的空频自适应图像复原","authors":"H. Lee, J. Paik","doi":"10.1109/APCAS.1996.569326","DOIUrl":null,"url":null,"abstract":"In this paper, we study examine the validity of space-frequency adaptive image restoration in the wavelet domain. In order to utilize adaptivity in both space and frequency domain, both the convolution operator and the signal is subband-decomposed using the wavelet transform, which maintains perfect reconstruction. A wiener-based wavelet decomposed image restoration technique has been proposed in the literature. In spite of outstanding restoration performance, wavelet decomposed wiener filter is restricted in use since it requires the original power spectrum and adaptive implementation is not easy. By this reason, we propose adaptive wavelet-decomposed CLS filters with some promising experimental results.","PeriodicalId":20507,"journal":{"name":"Proceedings of APCCAS'96 - Asia Pacific Conference on Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1996-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Space-frequency adaptive image restoration based on wavelet decomposition\",\"authors\":\"H. Lee, J. Paik\",\"doi\":\"10.1109/APCAS.1996.569326\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we study examine the validity of space-frequency adaptive image restoration in the wavelet domain. In order to utilize adaptivity in both space and frequency domain, both the convolution operator and the signal is subband-decomposed using the wavelet transform, which maintains perfect reconstruction. A wiener-based wavelet decomposed image restoration technique has been proposed in the literature. In spite of outstanding restoration performance, wavelet decomposed wiener filter is restricted in use since it requires the original power spectrum and adaptive implementation is not easy. By this reason, we propose adaptive wavelet-decomposed CLS filters with some promising experimental results.\",\"PeriodicalId\":20507,\"journal\":{\"name\":\"Proceedings of APCCAS'96 - Asia Pacific Conference on Circuits and Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of APCCAS'96 - Asia Pacific Conference on Circuits and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APCAS.1996.569326\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of APCCAS'96 - Asia Pacific Conference on Circuits and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APCAS.1996.569326","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Space-frequency adaptive image restoration based on wavelet decomposition
In this paper, we study examine the validity of space-frequency adaptive image restoration in the wavelet domain. In order to utilize adaptivity in both space and frequency domain, both the convolution operator and the signal is subband-decomposed using the wavelet transform, which maintains perfect reconstruction. A wiener-based wavelet decomposed image restoration technique has been proposed in the literature. In spite of outstanding restoration performance, wavelet decomposed wiener filter is restricted in use since it requires the original power spectrum and adaptive implementation is not easy. By this reason, we propose adaptive wavelet-decomposed CLS filters with some promising experimental results.