A NEW ALGORITHM FOR IMAGE NOISE-REDUCTION USING MATHEMATICAL MORPHOLOGY

Morphological openings and closings are useful for the smoothing of gray-scale images. However, their use for image noise reduction is limited by their tendency to remove important, thin features from an image along with the noise. This paper is a description and analysis of a new morphological image cleaning algorithm (MIG) that preserves thin features while removing noise, MIC is useful for gray-scale images corrupted by dense, low-amplitude, random, or patterned noise. Such noise is typical of scanned or still-video images, MIC differs from previous morphological noise filters in that it manipulates residual images-the differences between the original image and morphologically smoothed versions. It calculates residuals on a number of different scales via a morphological size distribution. It discards regions in the various residuals that it judges to contain noise. MIC creates a cleaned image by recombining the processed residual images with a smoothed version. This paper describes the MIC algorithm in detail, discusses the effects of parametric variations, presents the results of a noise analysis and shows a number of examples of its use, including the removal of scanner noise. The paper also demonstrates that MIC significantly improves the JPEG compression of a gray-scale image.