Filtering multiplicative noise in images using adaptive region-based statistics

Multiplicative noise is a type of signal-dependent noise where brighter areas of the images appear noisier. A popular class of image restoration methods is based on local mean, median, and variance. However, simple 3x3 filters do not take the nonstationary nature of the image and/or noise into account, and the restoration achieved by such filters may not be effective. We present a new adaptive-neighborhood or region-based noise filtering technique for restoring images with multiplicative noise. The method is based on finding variable-shaped, variable-sized adaptive neighborhoods for each pixel in the image, followed by the application of a filter specifically designed for multiplicative noise based on statistical parameters computed over the adaptive neighborhoods. From a visual inspection of restored images, if is clear that the proposed adaptive-neighborhood filter provides greater noise suppression than fixed-neighborhood restoration methods. The proposed method, unlike fixed-neighborhood methods, does not blur or clip object boundaries or corners. The mean squared errors between the results of the proposed method and the original images are considerably lower than those for results of the fixed-neighborhood methods studied, indicating that the image and noise statistics are better estimated by the adaptive-neighborhood method. (C) 1998 SPIE and 1998. [S1017-9909(98)02001-7].