Pub Date : 2024-03-01DOI: 10.1109/MSP.2024.3396646
Recounts the career and contributions of H. Joel Trussell.
介绍乔尔-特鲁塞尔（H. Joel Trussell）的职业生涯和贡献。
{"title":"In Memoriam: H. Joel Trussell [In Memoriam]","authors":"","doi":"10.1109/MSP.2024.3396646","DOIUrl":"https://doi.org/10.1109/MSP.2024.3396646","url":null,"abstract":"Recounts the career and contributions of H. Joel Trussell.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 2","pages":"102-102"},"PeriodicalIF":14.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10558735","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326320","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 : 2024-03-01DOI: 10.1109/MSP.2024.3349456
Breno Bahia;Arash JafarGandomi;Mauricio D. Sacchi
Vector-valued signals are crucial in science and engineering. The evolving field of hypercomplex signal processing, particularly quaternion algebra, offers a concise and natural approach to handling vectorial data. In multicomponent seismology, for instance, vector-valued signal processing finds a natural fit that has been exploited in several applications. This article provides a concise and practical review of quaternionic methods for handling vector-valued seismic datasets, from historical origins to key concepts and tools in the field of quaternion signal processing, such as the quaternion Fourier transform and quaternion singular value decomposition (SVD). While highlighting existing results, this review also showcases novel developments through source separation applications with quaternions, discussing encountered challenges and outlining potential future trends.
{"title":"Hypercomplex Processing of Vector Field Seismic Data: Toward vector-valued signal processing [Hypercomplex Signal and Image Processing]","authors":"Breno Bahia;Arash JafarGandomi;Mauricio D. Sacchi","doi":"10.1109/MSP.2024.3349456","DOIUrl":"https://doi.org/10.1109/MSP.2024.3349456","url":null,"abstract":"Vector-valued signals are crucial in science and engineering. The evolving field of hypercomplex signal processing, particularly quaternion algebra, offers a concise and natural approach to handling vectorial data. In multicomponent seismology, for instance, vector-valued signal processing finds a natural fit that has been exploited in several applications. This article provides a concise and practical review of quaternionic methods for handling vector-valued seismic datasets, from historical origins to key concepts and tools in the field of quaternion signal processing, such as the quaternion Fourier transform and quaternion singular value decomposition (SVD). While highlighting existing results, this review also showcases novel developments through source separation applications with quaternions, discussing encountered challenges and outlining potential future trends.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 2","pages":"29-41"},"PeriodicalIF":14.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326339","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.2024.3379753
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":"41 2","pages":"51-63"},"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":"41 2","pages":"11-13"},"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.2023.3345108
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":"41 1","pages":"14-17"},"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.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":"41 1","pages":"67-71"},"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}
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