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. The paper presents a description and analysis of a new morphological...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 1995-05, Vol.4 (5), p.554-568
Main Author: Peters, R.A.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Applied sciences
Cleaning
Exact sciences and technology
Filters
Gray-scale
Image analysis
Image coding
Image processing
Information, signal and communications theory
Microwave integrated circuits
Noise reduction
Signal processing
Smoothing methods
Telecommunications and information theory
Transform coding
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. The paper presents a description and analysis of a new morphological image cleaning algorithm (MIC) 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. The 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. It also demonstrates that MIC significantly improves the JPEG compression of a gray-scale image.< >
ISSN:1057-7149
1941-0042
DOI:10.1109/83.382491