Noise in an image is the unwanted information present; it should be removed without disturbing the useful information present in it. De-noising an image is very active research area in image processing. In this study, three classes of degraded noise images are used they are additive noise, multiplicative noise and impulsive noise. There are several algorithms for de-noise but each algorithm has its own assumptions, advantages and limitations. Histogram multithresholding give rise to explicit peaks, which reduces the task for finding thresholds in dissecting the image histogram. The proposed method uses histogram multithreshold segmentation as the first step followed by statistical features and pattern classifiers for identifying the noise type. Simple filters are used to get the noise samples and noise identification is achieved by using the proposed method. The proposed method yields the higher results when compared with the first method for classifying the noise types.

Date Published: 2 June 2012
PDF: 6 pages
Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83340G (2 June 2012); doi: 10.1117/12.946038

Published in SPIE Proceedings Vol. 8334:
Fourth International Conference on Digital Image Processing (ICDIP 2012)
Mohamed Othman; Sukumar Senthilkumar; Xie Yi, Editor(s)