Comments on: [tutorial] multispectral imaging, a case study http://hea-www.harvard.edu/AstroStat/slog/2008/multispectral-imaging-a-case-study/ Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders Fri, 01 Jun 2012 18:47:52 +0000 hourly 1 http://wordpress.org/?v=3.4 By: hlee http://hea-www.harvard.edu/AstroStat/slog/2008/multispectral-imaging-a-case-study/comment-page-1/#comment-793 hlee Mon, 13 Oct 2008 18:30:43 +0000 http://hea-www.harvard.edu/AstroStat/slog/?p=1018#comment-793 I don't think I understood your question except the part about "matrix inversion." Related to it, the topics on compressed sensing, random matrix, and sparse matrix are diversely discussed. Books and papers about statistical image/signal processing, or filter design, or solving nonlinear systems in engineering are related to getting feasible methods of inverting matrices. Once Prof. Babu said that it is hard to find unique research topics because one is likely to find similar problems already progressed in other fields. Only jargon distinguishes and differentiate them. The challenge is, as you always say, that it isn't easy to make other scientists understood/reformulate your problems into their terminology. I'm very much overwhelmed. :( I don’t think I understood your question except the part about “matrix inversion.” Related to it, the topics on compressed sensing, random matrix, and sparse matrix are diversely discussed. Books and papers about statistical image/signal processing, or filter design, or solving nonlinear systems in engineering are related to getting feasible methods of inverting matrices. Once Prof. Babu said that it is hard to find unique research topics because one is likely to find similar problems already progressed in other fields. Only jargon distinguishes and differentiate them. The challenge is, as you always say, that it isn’t easy to make other scientists understood/reformulate your problems into their terminology. I’m very much overwhelmed. :(

]]> By: vlk http://hea-www.harvard.edu/AstroStat/slog/2008/multispectral-imaging-a-case-study/comment-page-1/#comment-788 vlk Mon, 13 Oct 2008 17:20:44 +0000 http://hea-www.harvard.edu/AstroStat/slog/?p=1018#comment-788 This is indeed a commonly encountered astronomical situation, and not just for photometry. <a href="http://hea-www.harvard.edu/AstroStat/slog/2008/eotw-dem/" rel="nofollow">DEMs</a> too, for example. But one of the major problems with "inverting" that matrix equation is in the high-frequency instability. How do the signal processing people deal with that? If there is a way to tamp it down based on the characteristics of the error term, that may be very useful. This is indeed a commonly encountered astronomical situation, and not just for photometry. DEMs too, for example.

But one of the major problems with “inverting” that matrix equation is in the high-frequency instability. How do the signal processing people deal with that? If there is a way to tamp it down based on the characteristics of the error term, that may be very useful.

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