Frequency-aware SVD decomposition and its application to color magnification and motion denoising

Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singul...

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Main Authors: Kajo, I., Kamel, N., Ruichek, Y., Al-Ahdal, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29454 /
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112621426&doi=10.1109%2fACCESS.2021.3101823&partnerID=40&md5=7fa9b65a6ecdc158652a71521097300f
http://eprints.utp.edu.my/29454/
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spelling utp-eprints.294542022-03-25T02:07:08Z Frequency-aware SVD decomposition and its application to color magnification and motion denoising Kajo, I. Kamel, N. Ruichek, Y. Al-Ahdal, A. Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposition schemes to clearly reveal such changes. The first scheme involves enhancing the visual characteristics of small subtle color changes in the presence of a wide variety of motion patterns by magnifying their pixel intensities. The second scheme removes short-term motions that visually distract attention from the underlying content of video sequences such as time-lapse videos, snowing scene, and maritime surveillance. Both schemes involve the decomposition of videos into spatiotemporal slices in which each slice is further decomposed into several singular components. The low-rank components that primarily represent background and color intensity information are then temporally processed to magnify the magnitude of the signal at the subtle color change target frequency. At the same time, an approach similar to that used in denoising time-lapse sequences is applied to temporally filter the singular components representing sparse information, thereby removing jittery short-term motions while preserving long-term motions, which are represented by both low-rank and unfiltered sparse components. We demonstrate promising color magnification and motion denoising results that can be obtained much faster than results estimated using state-of-the-art techniques. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. Institute of Electrical and Electronics Engineers Inc. 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112621426&doi=10.1109%2fACCESS.2021.3101823&partnerID=40&md5=7fa9b65a6ecdc158652a71521097300f Kajo, I. and Kamel, N. and Ruichek, Y. and Al-Ahdal, A. (2021) Frequency-aware SVD decomposition and its application to color magnification and motion denoising. IEEE Access, 9 . pp. 108832-108845. http://eprints.utp.edu.my/29454/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposition schemes to clearly reveal such changes. The first scheme involves enhancing the visual characteristics of small subtle color changes in the presence of a wide variety of motion patterns by magnifying their pixel intensities. The second scheme removes short-term motions that visually distract attention from the underlying content of video sequences such as time-lapse videos, snowing scene, and maritime surveillance. Both schemes involve the decomposition of videos into spatiotemporal slices in which each slice is further decomposed into several singular components. The low-rank components that primarily represent background and color intensity information are then temporally processed to magnify the magnitude of the signal at the subtle color change target frequency. At the same time, an approach similar to that used in denoising time-lapse sequences is applied to temporally filter the singular components representing sparse information, thereby removing jittery short-term motions while preserving long-term motions, which are represented by both low-rank and unfiltered sparse components. We demonstrate promising color magnification and motion denoising results that can be obtained much faster than results estimated using state-of-the-art techniques. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
format Article
author Kajo, I.
Kamel, N.
Ruichek, Y.
Al-Ahdal, A.
spellingShingle Kajo, I.
Kamel, N.
Ruichek, Y.
Al-Ahdal, A.
Frequency-aware SVD decomposition and its application to color magnification and motion denoising
author_sort Kajo, I.
title Frequency-aware SVD decomposition and its application to color magnification and motion denoising
title_short Frequency-aware SVD decomposition and its application to color magnification and motion denoising
title_full Frequency-aware SVD decomposition and its application to color magnification and motion denoising
title_fullStr Frequency-aware SVD decomposition and its application to color magnification and motion denoising
title_full_unstemmed Frequency-aware SVD decomposition and its application to color magnification and motion denoising
title_sort frequency-aware svd decomposition and its application to color magnification and motion denoising
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112621426&doi=10.1109%2fACCESS.2021.3101823&partnerID=40&md5=7fa9b65a6ecdc158652a71521097300f
http://eprints.utp.edu.my/29454/
_version_ 1741197244174958592
score 11.62408

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