{"title":"Multiplexed fluorescence unmixing","authors":"Marina Alterman, Y. Schechner, A. Weiss","doi":"10.1109/ICCPHOT.2010.5585093","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2010.5585093","url":null,"abstract":"Multiplexed imaging and illumination have been used to recover enhanced arrays of intensity or spectral reflectance samples, per pixel. However, these arrays are often not the ultimate goal of a system, since the intensity is a result of underlying object characteristics, which interest the user. For example, spectral reflectance, emission or absorption distributions stem from an underlying mixture of materials. Therefore, systems try to infer concentrations of these underlying mixed components. Thus, computational analysis does not end with recovery of intensity (or equivalent) arrays. Inversion of mixtures, termed unmixing, is central to many problems. We incorporate the mixing/unmixing process explicitly into the optimization of multiplexing codes. This way, optimal recovery of the underlying components (materials) is directly sought. Without this integrated approach, multiplexing can even degrade the unmixing result. Moreover, by directly defining the goal of data acquisition to be recovery of components (materials) rather than of intensity arrays, the acquisition becomes more efficient. This yields significant generalizations of multiplexing theory. We apply this approach to fluorescence imaging.","PeriodicalId":248821,"journal":{"name":"2010 IEEE International Conference on Computational Photography (ICCP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115965768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Computational photography and compressive holography","authors":"D. Marks, Joonku Hahn, R. Horisaki, D. Brady","doi":"10.1109/ICCPHOT.2010.5585090","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2010.5585090","url":null,"abstract":"As lasers, photosensors, and computational imaging techniques improve, holography becomes an increasingly attractive approach for imaging applications largely reserved for photography. For the same illumination energy, we show that holography and photography have nearly identical noise performance. Because the coherent field is two dimensional outside of a source, there is ambiguity in inferring the three-dimensional structure of a source from the coherent field. Compressive holography overcomes this limitation by imposing sparsity constraints on the three-dimensional scatterer, which greatly reduces the number of possibilities allowing reliable inference of structure. We demonstrate the use of compressive holography to infer the three-dimensional structure of a scene comprising two toys.","PeriodicalId":248821,"journal":{"name":"2010 IEEE International Conference on Computational Photography (ICCP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127823706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Recovering color from black and white photographs","authors":"S. Olsen, Rachel Gold, A. Gooch, B. Gooch","doi":"10.1109/ICCPHOT.2010.5585088","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2010.5585088","url":null,"abstract":"This paper presents a mathematical framework for recovering color information from multiple photographic sources. Such sources could include either black and white negatives or photographic plates. This paper's main technical contribution is the use of Bayesian analysis to calculate the most likely color at any sample point, along with an expected error value. We explore the limits of our approach using hyperspectral datasets, and show that in some cases, it may be possible to recover the bulk of the color information in an image from as few as two black and white sources.","PeriodicalId":248821,"journal":{"name":"2010 IEEE International Conference on Computational Photography (ICCP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129561970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}