@inproceedings{Urban2008_2,<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Abstract = {Wiener filtering has many applications in the area of imaging
science. In image processing, for instance, it is a common
way of reducing Gaussian noise. In color science it is often used
to estimate reflectances from camera response data on a pixel by
pixel basis. Based on a priori assumptions the Wiener filter is the
optimal linear filter in the sense of the minimal mean square error
to the actual data. In this paper we propose a spatially adaptive
Wiener filter to estimate reflectances from images captured by a
multispectral camera. The filter estimates pixel noise using local
spatial neighborhood and uses this knowledge to estimate a spectral
reflectance. In the hypothetical case of a noiseless system, the
spatially adaptive Wiener filter equals the standard Wiener filter
for reflectance estimation. We present results of various simulation
experiments conducted on a multispectral image database
using a 6-channel acquisition system and different noise levels.},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Address = {Portland, Oregon},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Author = {Philipp Urban and Mitchell R.  Rosen and Roy S.  Berns},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Booktitle = {, IS&T/SID Color Imaging Conference},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Keywords = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Month = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Organization = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Pages = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Title = {A Spatially Adaptive Wiener Filter for Reflectance Estimation},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Url = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Volume = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Year = {2008}