Back to basics: Fast denoising iterative algorithm
We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. Our method is computationally efficient, does not require training or ground truth data, and can be applied in the presence of independent noise, as well as correlated (coherent) noise, where the noise level is unknow...
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| Published in: | Signal processing Vol. 221; p. 109482 |
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| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.08.2024
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| ISSN: | 0165-1684, 1872-7557 |
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| Abstract | We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. Our method is computationally efficient, does not require training or ground truth data, and can be applied in the presence of independent noise, as well as correlated (coherent) noise, where the noise level is unknown. We examine three study cases: natural image denoising in the presence of additive white Gaussian noise, Poisson-distributed image denoising, and speckle suppression in optical coherence tomography (OCT). Experimental results demonstrate that the proposed approach can effectively improve image quality, in challenging noise settings. Theoretical guarantees are provided for convergence stability.
•We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction.•Our method is computationally efficient and does not require ground truth data.•BTB can mitigate independent or correlated noise with unknown noise level.•Experimental results demonstrate improved image quality, in challenging noise settings.•Theoretical guarantees are provided for convergence stability. |
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| AbstractList | We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. Our method is computationally efficient, does not require training or ground truth data, and can be applied in the presence of independent noise, as well as correlated (coherent) noise, where the noise level is unknown. We examine three study cases: natural image denoising in the presence of additive white Gaussian noise, Poisson-distributed image denoising, and speckle suppression in optical coherence tomography (OCT). Experimental results demonstrate that the proposed approach can effectively improve image quality, in challenging noise settings. Theoretical guarantees are provided for convergence stability.
•We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction.•Our method is computationally efficient and does not require ground truth data.•BTB can mitigate independent or correlated noise with unknown noise level.•Experimental results demonstrate improved image quality, in challenging noise settings.•Theoretical guarantees are provided for convergence stability. |
| ArticleNumber | 109482 |
| Author | Pereg, Deborah |
| Author_xml | – sequence: 1 givenname: Deborah orcidid: 0000-0002-2453-6577 surname: Pereg fullname: Pereg, Deborah email: deborahp@mit.edu, dvorapereg@gmail.com organization: MIT MechE, Harvard School of Engineering and Applied Sciences, United States of America |
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| CitedBy_id | crossref_primary_10_1016_j_compmedimag_2025_102596 |
| Cites_doi | 10.1109/ICCVW60793.2023.00412 10.3390/jimaging9110237 10.1002/cpa.20042 10.1109/ICCVW54120.2021.00213 10.1109/MSP.2008.930649 10.1364/BOE.9.000486 10.1109/ICCV.2001.937655 10.1137/140990978 10.1117/1.1578087 10.1137/16M1102884 10.1109/TIP.2010.2053941 10.1038/s41598-019-51062-7 10.5201/ipol.2011.bcm_nlm 10.1016/j.optcom.2012.10.053 10.1016/j.jvcir.2016.09.009 10.1002/jbio.201960135 10.1109/CVPR.2018.00652 10.1364/OL.41.000994 10.1109/TIP.2017.2662206 10.1364/OE.15.006200 10.1016/0022-247X(73)90087-5 10.1137/080716542 10.1088/1361-6560/ab3556 10.1007/s11042-019-07999-y 10.1007/978-3-030-01252-6_1 10.1109/TIP.2013.2283400 10.1364/BOE.9.003354 10.2307/3318418 10.1109/TIP.2006.881969 10.1364/BOE.9.005129 10.4064/fm-3-1-133-181 10.1016/j.neunet.2023.08.032 10.1109/TPAMI.2016.2596743 10.1117/1.429925 10.1002/jemt.20294 |
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| Keywords | Speckle suppression Inverse problems Fixed-point Image denoising |
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