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M. Berman, "Automated Smoothing of Image and Other Regularly Spaced Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 460468, May, 1994.  
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@article{ 10.1109/34.291451, author = {M. Berman}, title = {Automated Smoothing of Image and Other Regularly Spaced Data}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {16}, number = {5}, issn = {01628828}, year = {1994}, pages = {460468}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.291451}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Automated Smoothing of Image and Other Regularly Spaced Data IS  5 SN  01628828 SP460 EP468 EPD  460468 A1  M. Berman, PY  1994 KW  remote sensing; Fourier transforms; filtering and prediction theory; image processing; approximation theory; splines (mathematics); high dimensional remote sensing imagery; noise reduction; image smoothing; generalized cross validation; thin plate smoothing spline; Fourier transform; space domain approximations VL  16 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
This paper is primarily motivated by the problem of automatically removing unwanted noise from highdimensional remote sensing imagery. The initial step involves the transformation of the data to a space of intrinsically lower dimensionality and the smoothing of images in the new space. Different images require different amounts of smoothing. The signal (assumed to be mostly smooth with relatively few discontinuities) is estimated from the data using the method of generalized crossvalidation. It is shown how the generalized crossvalidated thinplate smoothing spline with observations on a regular grid (in ddimensions) is easily approximated and computed in the Fourier domain. Space domain approximations are also investigated. The technique is applied to some remote sensing data.
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