Mixed Noise Reduction based on Improved PCNN Algorithm

Authors: Shajun Nisha, Kother Mohideen

DOI: https://doi.org/10.17605/OSF.IO/QN2E5

Short DOI: https://doi.org/ggmpbk

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Abstract: The image de-noising naturally corrupted by noise is a classical problem in the field of signal or image processing. Additive random noise can easily be removed using simple threshold methods. De-noising of natural images corrupted by Gaussian noise and Gaussian - Gaussian Mixture using wavelet techniques are very effective because of its ability to capture the energy of a signal in few energy transform values. In this paper decompose the image using discrete wavele and then applied PCNN (Pulse Coupled Neural Network) algorithm and threshold for mixed noise removal. The proposed method can efficiently remove a variety of mixed or single noise while preserving the image information well. It is proposed to investigate the suitability of different wavelet bases and the size of different neighborhood on the performance of image de-noising algorithms in terms of PSNR. The experimental results demonstrate its better performance compared with some existing methods.

Keywords: Image Transform, De-noising, Wavelet, PCNN, Mixed Image Noise

Paper Id: 27

Published On: 2014-11-03

Published In: Volume 2, Issue 6, November-December 2014

Cite This: Mixed Noise Reduction based on Improved PCNN Algorithm - Shajun Nisha, Kother Mohideen - IJIRMPS Volume 2, Issue 6, November-December 2014. DOI 10.17605/OSF.IO/QN2E5

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