Compressive sensing-based image denoising using adaptive multiple sampling and optimal error tolerance. Kang, W., Lee, E., Chea, E., Katsaggelos, A. K., & Paik, J. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 2503–2507, may, 2013. IEEE. ![Compressive sensing-based image denoising using adaptive multiple sampling and optimal error tolerance [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method. © 2013 IEEE.
@inproceedings{Wonseok2013a,
abstract = {In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method. {\textcopyright} 2013 IEEE.},
author = {Kang, Wonseok and Lee, Eunsung and Chea, Eunjung and Katsaggelos, Aggelos K. and Paik, Joonki},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
doi = {10.1109/ICASSP.2013.6638106},
isbn = {978-1-4799-0356-6},
issn = {15206149},
keywords = {Compressed sensing,image denoising,matching pursuit algorithms},
month = {may},
pages = {2503--2507},
publisher = {IEEE},
title = {{Compressive sensing-based image denoising using adaptive multiple sampling and optimal error tolerance}},
url = {http://ieeexplore.ieee.org/document/6638106/},
year = {2013}
}
Downloads: 0
{"_id":"MpZbqRn53heAus6pr","bibbaseid":"kang-lee-chea-katsaggelos-paik-compressivesensingbasedimagedenoisingusingadaptivemultiplesamplingandoptimalerrortolerance-2013","author_short":["Kang, W.","Lee, E.","Chea, E.","Katsaggelos, A. K.","Paik, J."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","abstract":"In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method. © 2013 IEEE.","author":[{"propositions":[],"lastnames":["Kang"],"firstnames":["Wonseok"],"suffixes":[]},{"propositions":[],"lastnames":["Lee"],"firstnames":["Eunsung"],"suffixes":[]},{"propositions":[],"lastnames":["Chea"],"firstnames":["Eunjung"],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos","K."],"suffixes":[]},{"propositions":[],"lastnames":["Paik"],"firstnames":["Joonki"],"suffixes":[]}],"booktitle":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","doi":"10.1109/ICASSP.2013.6638106","isbn":"978-1-4799-0356-6","issn":"15206149","keywords":"Compressed sensing,image denoising,matching pursuit algorithms","month":"may","pages":"2503–2507","publisher":"IEEE","title":"Compressive sensing-based image denoising using adaptive multiple sampling and optimal error tolerance","url":"http://ieeexplore.ieee.org/document/6638106/","year":"2013","bibtex":"@inproceedings{Wonseok2013a,\nabstract = {In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method. {\\textcopyright} 2013 IEEE.},\nauthor = {Kang, Wonseok and Lee, Eunsung and Chea, Eunjung and Katsaggelos, Aggelos K. and Paik, Joonki},\nbooktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},\ndoi = {10.1109/ICASSP.2013.6638106},\nisbn = {978-1-4799-0356-6},\nissn = {15206149},\nkeywords = {Compressed sensing,image denoising,matching pursuit algorithms},\nmonth = {may},\npages = {2503--2507},\npublisher = {IEEE},\ntitle = {{Compressive sensing-based image denoising using adaptive multiple sampling and optimal error tolerance}},\nurl = {http://ieeexplore.ieee.org/document/6638106/},\nyear = {2013}\n}\n","author_short":["Kang, W.","Lee, E.","Chea, E.","Katsaggelos, A. K.","Paik, J."],"key":"Wonseok2013a","id":"Wonseok2013a","bibbaseid":"kang-lee-chea-katsaggelos-paik-compressivesensingbasedimagedenoisingusingadaptivemultiplesamplingandoptimalerrortolerance-2013","role":"author","urls":{"Paper":"http://ieeexplore.ieee.org/document/6638106/"},"keyword":["Compressed sensing","image denoising","matching pursuit algorithms"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://sites.northwestern.edu/ivpl/files/2023/06/IVPL_Updated_publications-1.bib","dataSources":["KTWAakbPXLGfYseXn","ePKPjG8C6yvpk4mEK","ya2CyA73rpZseyrZ8","D8k2SxfC5dKNRFgro","7Dwzbxq93HWrJEhT6","qhF8zxmGcJfvtdeAg","fvDEHD49E2ZRwE3fb","H7crv8NWhZup4d4by","DHqokWsryttGh7pJE","vRJd4wNg9HpoZSMHD","sYxQ6pxFgA59JRhxi","w2WahSbYrbcCKBDsC","XasdXLL99y5rygCmq","3gkSihZQRfAD2KBo3","t5XMbyZbtPBo4wBGS","bEpHM2CtrwW2qE8FP","teJzFLHexaz5AQW5z"],"keywords":["compressed sensing","image denoising","matching pursuit algorithms"],"search_terms":["compressive","sensing","based","image","denoising","using","adaptive","multiple","sampling","optimal","error","tolerance","kang","lee","chea","katsaggelos","paik"],"title":"Compressive sensing-based image denoising using adaptive multiple sampling and optimal error tolerance","year":2013}