A modified nonlocal-means for adaptive image denoising

Image denoising is an important problem and widely studied in image processing. Denoising is a crucial step to increase image conspicuity and to improve the performances of all the processing needed for quantitative imaging analysis. This work presents a modified nonlocal-mean denoising method based...

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Main Authors: Ming Yin, Shi-Quan Shao, Li Ma
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
edge information
Image denoising
image denosing
Image edge detection
Information filters
Noise
Noise reduction
nonlocal means
PSNR
Signal processing algorithms
texture collection
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Shi-Quan Shao
Li Ma
description Image denoising is an important problem and widely studied in image processing. Denoising is a crucial step to increase image conspicuity and to improve the performances of all the processing needed for quantitative imaging analysis. This work presents a modified nonlocal-mean denoising method based on the texture collection and edge information. At last, experiments result illustrate that the technique can be successfully used to the classical case of additive Gaussian noise. The proposed algorithm seems to improve on the state of the art performance.
doi_str_mv 10.1109/EMEIT.2011.6023978
format conference_proceeding
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subjects Algorithm design and analysis
edge information
Image denoising
image denosing
Image edge detection
Information filters
Noise
Noise reduction
nonlocal means
PSNR
Signal processing algorithms
texture collection
title A modified nonlocal-means for adaptive image denoising
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