Morphological openings and closings are useful for the smoothing of grayscale 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 (MIC) that preserves thin features while removing noise.
MIC is useful for grayscale 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 grayscale image.
Source: Sofia University
Authors: Richard Alan Peters II