In many image-processing applications the noise that corrupts the imag
es is signal dependent, the most widely encountered types being multip
licative, Poisson, film-grain, and speckle noise. Their common feature
is that the power of the noise is related to the brightness of the co
rrupted pixel. This results in brighter areas appearing to be noisier
than darker areas. We propose a new adaptive-neighborhood approach to
filtering images corrupted by signal-dependent noise. Instead of using
fixed-size, fixed-shape neighborhoods, statistics of the noise and th
e signal are computed within variable-size, variable-shape neighborhoo
ds that are grown for every pixel to contain only pixels that belong t
o the same object. Results of adaptive-neighborhood filtering are comp
ared with those given by two local-statistics-based filters (the refin
ed Lee filter and the noise-updating repeated Wiener filter), both in
terms of subjective and objective measures. The adaptive-neighborhood
approach provides better noise suppression as indicated by lower mean-
squared errors as well as better retention of edge sharpness than the
other approaches considered. (C) 1998 Optical Society of America.