Literature DB >> 16279175

A universal noise removal algorithm with an impulse detector.

Roman Garnett1, Timothy Huegerich, Charles Chui, Wenjie He.   

Abstract

We introduce a local image statistic for identifying noise pixels in images corrupted with impulse noise of random values. The statistical values quantify how different in intensity the particular pixels are from their most similar neighbors. We continue to demonstrate how this statistic may be incorporated into a filter designed to remove additive Gaussian noise. The result is a new filter capable of reducing both Gaussian and impulse noises from noisy images effectively, which performs remarkably well, both in terms of quantitative measures of signal restoration and qualitative judgements of image quality. Our approach is extended to automatically remove any mix of Gaussian and impulse noise.

Mesh:

Year:  2005        PMID: 16279175     DOI: 10.1109/tip.2005.857261

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  12 in total

1.  Optimal Weights Mixed Filter for removing mixture of Gaussian and impulse noises.

Authors:  Qiyu Jin; Ion Grama; Quansheng Liu
Journal:  PLoS One       Date:  2017-07-10       Impact factor: 3.240

2.  Removal of random-valued impulse noise from Cerenkov luminescence images.

Authors:  Duofang Chen; Shouping Zhu; Yi Huang; Jimin Liang; Xueli Chen
Journal:  Med Biol Eng Comput       Date:  2019-11-21       Impact factor: 2.602

3.  An approach to improve the quality of infrared images of vein-patterns.

Authors:  Chih-Lung Lin
Journal:  Sensors (Basel)       Date:  2011-12-01       Impact factor: 3.576

4.  A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal.

Authors:  Hongyao Deng; Qingxin Zhu; Xiuli Song; Jinsong Tao
Journal:  Comput Intell Neurosci       Date:  2017-04-27

5.  Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images.

Authors:  Krystian Radlak; Lukasz Malinski; Bogdan Smolka
Journal:  Sensors (Basel)       Date:  2020-05-14       Impact factor: 3.576

6.  A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation.

Authors:  Jing Fang; Shaohai Hu; Xiaole Ma
Journal:  Sensors (Basel)       Date:  2018-10-13       Impact factor: 3.576

7.  Efficient Denoising Framework for Mammogram Images with a New Impulse Detector and Non-Local Means.

Authors:  Harikumar Rajaguru; Sannasi Chakravarthy S R
Journal:  Asian Pac J Cancer Prev       Date:  2020-01-01

8.  An efficient method to remove mixed Gaussian and random-valued impulse noise.

Authors:  Mengdi Xing; Guorong Gao
Journal:  PLoS One       Date:  2022-03-03       Impact factor: 3.240

9.  The augmented lagrange multipliers method for matrix completion from corrupted samplings with application to mixed Gaussian-impulse noise removal.

Authors:  Fan Meng; Xiaomei Yang; Chenghu Zhou
Journal:  PLoS One       Date:  2014-09-23       Impact factor: 3.240

Review 10.  Two-step verification of brain tumor segmentation using watershed-matching algorithm.

Authors:  Mohiudding Ahmad; S M Kamrul Hasan
Journal:  Brain Inform       Date:  2018-08-14
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