Pub Date : 2024-03-01DOI: 10.1109/MSP.2024.3379753
Neil D. Dizon;Jeffrey A. Hogan
Recently, novel quaternion-valued wavelets on the plane were constructed using an optimization approach. These wavelets are compactly supported, smooth, orthonormal, nonseparable, and truly quaternionic. However, they have not been tested in application. In this article, we introduce a methodology for decomposing and reconstructing color images using quaternionic wavelet filters associated to recently developed quaternion-valued wavelets on the plane. We investigate the applicability of this method in the compression, enhancement, segmentation, and denoising of color images. Our results demonstrate these wavelets as promising tools for an end-to-end quaternion processing of color images.
{"title":"Holistic Processing of Color Images Using Novel Quaternion-Valued Wavelets on the Plane: A promising transformative tool [Hypercomplex Signal and Image Processing]","authors":"Neil D. Dizon;Jeffrey A. Hogan","doi":"10.1109/MSP.2024.3379753","DOIUrl":"https://doi.org/10.1109/MSP.2024.3379753","url":null,"abstract":"Recently, novel quaternion-valued wavelets on the plane were constructed using an optimization approach. These wavelets are compactly supported, smooth, orthonormal, nonseparable, and truly quaternionic. However, they have not been tested in application. In this article, we introduce a methodology for decomposing and reconstructing color images using quaternionic wavelet filters associated to recently developed quaternion-valued wavelets on the plane. We investigate the applicability of this method in the compression, enhancement, segmentation, and denoising of color images. Our results demonstrate these wavelets as promising tools for an end-to-end quaternion processing of color images.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.1109/MSP.2024.3378129
Nektarios A. Valous;Eckhard Hitzer;Salvatore Vitabile;Swanhild Bernstein;Carlile Lavor;Derek Abbott;Maria Elena Luna-Elizarrarás;Wilder Lopes
Novel computational signal and image analysis methodologies based on feature-rich mathematical/computational frameworks continue to push the limits of the technological envelope, thus providing optimized and efficient solutions. Hypercomplex signal and image processing is a fascinating field that extends conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. Methodologies that are developed within this field can lead to more effective and powerful ways to analyze signals and images. Processing audio, video, images, and other types of data in the hypercomplex domain allows for more complex and intuitive representations with algebraic properties that can lead to new insights and optimizations. Applications in image processing, signal filtering, and deep learning (just to name a few) have shown that working in the hypercomplex domain can lead to more efficient and robust outcomes. As research in this field progresses and software tools become more widely available, we can expect to see increasingly sophisticated applications in many areas of research, e.g., computer vision, machine learning, and so on.
{"title":"Hypercomplex Signal and Image Processing: Part 1 [From the Guest Editors]","authors":"Nektarios A. Valous;Eckhard Hitzer;Salvatore Vitabile;Swanhild Bernstein;Carlile Lavor;Derek Abbott;Maria Elena Luna-Elizarrarás;Wilder Lopes","doi":"10.1109/MSP.2024.3378129","DOIUrl":"https://doi.org/10.1109/MSP.2024.3378129","url":null,"abstract":"Novel computational signal and image analysis methodologies based on feature-rich mathematical/computational frameworks continue to push the limits of the technological envelope, thus providing optimized and efficient solutions. Hypercomplex signal and image processing is a fascinating field that extends conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. Methodologies that are developed within this field can lead to more effective and powerful ways to analyze signals and images. Processing audio, video, images, and other types of data in the hypercomplex domain allows for more complex and intuitive representations with algebraic properties that can lead to new insights and optimizations. Applications in image processing, signal filtering, and deep learning (just to name a few) have shown that working in the hypercomplex domain can lead to more efficient and robust outcomes. As research in this field progresses and software tools become more widely available, we can expect to see increasingly sophisticated applications in many areas of research, e.g., computer vision, machine learning, and so on.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10558747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/MSP.2023.3335893
Branko Ristic;Alessio Benavoli;Sanjeev Arulampalam
Bayes’ rule, as one of the fundamental concepts of statistical signal processing, provides a way to update our belief about an event based on the arrival of new pieces of evidence. Uncertainty is traditionally modeled by a probability distribution. Prior belief is thus expressed by a prior probability distribution, while the update involves the likelihood function, a probabilistic expression of how likely it is to observe the evidence. It has been argued by many statisticians, however, that a broadening of probability theory is required because one may not always be able to provide a probability for every event, due to the scarcity of training data.
{"title":"Bayes’ Rule Using Imprecise Probabilities [Lecture Notes]","authors":"Branko Ristic;Alessio Benavoli;Sanjeev Arulampalam","doi":"10.1109/MSP.2023.3335893","DOIUrl":"https://doi.org/10.1109/MSP.2023.3335893","url":null,"abstract":"Bayes’ rule, as one of the fundamental concepts of statistical signal processing, provides a way to update our belief about an event based on the arrival of new pieces of evidence. Uncertainty is traditionally modeled by a probability distribution. Prior belief is thus expressed by a prior probability distribution, while the update involves the likelihood function, a probabilistic expression of how likely it is to observe the evidence. It has been argued by many statisticians, however, that a broadening of probability theory is required because one may not always be able to provide a probability for every event, due to the scarcity of training data.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140559418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/MSP.2023.3345108
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
提供从业人员和研究人员感兴趣的社会信息，包括新闻、评论或技术说明。
{"title":"Kerala Chapter Receives the 2023 Chapter of the Year Award! [Society News]","authors":"","doi":"10.1109/MSP.2023.3345108","DOIUrl":"https://doi.org/10.1109/MSP.2023.3345108","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10502216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140559281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/MSP.2024.3362573
Presents the recipients of IEEE Signal Processing Society awards for 2023.
介绍电气和电子工程师学会信号处理学会 2023 年度获奖者。
{"title":"2023 IEEE Signal Processing Society Awards [Society News]","authors":"","doi":"10.1109/MSP.2024.3362573","DOIUrl":"https://doi.org/10.1109/MSP.2024.3362573","url":null,"abstract":"Presents the recipients of IEEE Signal Processing Society awards for 2023.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10501956","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140559416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/MSP.2023.3326886
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
提供从业人员和研究人员感兴趣的社会信息，包括新闻、评论或技术说明。
{"title":"IEEE SPS 2023 President-Elect, Members-at-Large, and Regional Directors-at-Large Election Results [Society News]","authors":"","doi":"10.1109/MSP.2023.3326886","DOIUrl":"https://doi.org/10.1109/MSP.2023.3326886","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10502202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140559426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/MSP.2024.3368239
Sin-Wei Chiu;Keshab K. Parhi
This tutorial aims to establish connections between polynomial modular multiplication over a ring to circular convolution and the discrete Fourier transform (DFT). The main goal is to extend the well-known theory of the DFT in signal processing (SP) to other applications involving polynomials in a ring, such as homomorphic encryption (HE).
{"title":"Long Polynomial Modular Multiplication Using Low-Complexity Number Theoretic Transform [Lecture Notes]","authors":"Sin-Wei Chiu;Keshab K. Parhi","doi":"10.1109/MSP.2024.3368239","DOIUrl":"https://doi.org/10.1109/MSP.2024.3368239","url":null,"abstract":"This tutorial aims to establish connections between polynomial modular multiplication over a ring to circular convolution and the discrete Fourier transform (DFT). The main goal is to extend the well-known theory of the DFT in signal processing (SP) to other applications involving polynomials in a ring, such as homomorphic encryption (HE).","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140559305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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