G. Dhivya, Saveetha Engineering College, Chennai, Tamilnadu, India.
DOI : 01.0401/ijaict.2014.01.26
International Journal of Advanced Information and Communication Technology
Received On : August 16, 2014
Revised On : September 23, 2014
Accepted On : October 12, 2014
Published On : November 05, 2014
Volume 01, Issue 07
Pages : 135-139
Abstract
Noise removal and image enhancement are theimportant tasks addressed by many Image Processing algorithms,especially, when the images are corrupted by high noise level e.g. inthe case of remote imaging, thermal imaging, night vision etc. Image denoising has been a well studied problem in the field of image processing. Denoising technique is a pre-processing step in compression, segmentation and restoration. Denoising is classified into two types: Local and Non local means. The presence of similar patterns and features in an image are referred to as Non Localmeans. Non local means algorithm assumes that the image contains excessive redundancy and these redundancies can be used to remove the noise present in the image. It estimates noise-free pixel intensity as a weighted average of all pixel intensities in the image, and the weights are proportional to the similarity between the local neighborhoods. The recently proposed non local means achieves excellent performance in digital image processing. In addition to the conventional non local means, a new technique called improved non local means has been explored. By using pre-classification, similar block searching and weighted averaging, the INLM filtering is more efficient than conventional NLM.
Keywords
Denoising, Local and Non local means, Gaussian Noise, Pre-classification.
Cite this article
G. Dhivya, “Improved Non Local Means to Remove Gaussian Noise in Natural Images” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.135-139, November 05, 2014.
Copyright
© 2014 G. Dhivya. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.