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Telecommunications and Radio Engineering

Publicou 12 edições por ano

ISSN Imprimir: 0040-2508

ISSN On-line: 1943-6009

SJR: 0.185 SNIP: 0.268 CiteScore™:: 1.5 H-Index: 22

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Methods for Blind Evaluation of Noise Variance in Multichannel Optical and Radar Images

Volume 65, Edição 6-10, 2006, pp. 527-556
DOI: 10.1615/TelecomRadEng.v65.i6.40
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RESUMO

A priori knowledge of noise type and statistical characteristics (at least, noise variance) is a pre-requisite of successful solving many practical tasks of image processing like filtering (denoising), edge detection, segmentation, etc. This relates to both gray-scale and multichannel (multi- and hyper-spectral) images. However, in practical situations noise characteristics depend upon many factors and it is a common practice to determine noise type and variance just for images at hand. It is desirable to perform this operation automatically especially if one deals with multichannel images. Thus, below we consider several approaches and methods for blind evaluation of noise variance. Their accuracy and applicability are analyzed for images with different properties, in particular, various percentage of texture regions. Besides, a set of noise variance values is tested.

CITADO POR
  1. Ponomarenko Nikolay, Krivenko Sergey, Lukin Vladimir, Egiazarian Karen, Astola Jaakko T., Lossy Compression of Noisy Images Based on Visual Quality: A Comprehensive Study, EURASIP Journal on Advances in Signal Processing, 2010, 1, 2010. Crossref

  2. Lukin Vladimir V., Methods and automatic procedures for processing images based on blind evaluation of noise type and characteristics, Journal of Applied Remote Sensing, 5, 1, 2011. Crossref

  3. Abramov Sergey, Zabrodina Victoriya, Lukin Vladimir, Vozel Benoit, Chehdi Kacem, Astola Jaakko, Improved method for blind estimation of the variance of mixed noise using weighted LMS line fitting algorithm, Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010. Crossref

  4. Lukin V.V., Abramov S.K., Vozel Benoit, Uss Mikhail, Chehdi Kacem, Performance analys of segmentation-based method for blind evaluation of additive noise in images, 2010 INTERNATIONAL KHARKOV SYMPOSIUM ON PHYSICS AND ENGINEERING OF MICROWAVES, MILLIMETER AND SUBMILLIMETER WAVES, 2010. Crossref

  5. Abramov Sergey K., Segmentation-based method for blind evaluation of noise variance in images, Journal of Applied Remote Sensing, 2, 1, 2008. Crossref

  6. Popov Mikhail A., Stankevich Sergey A., Lischenko Ludmila P., Lukin Vladimir V., Ponomarenko Nikolay N., Processing of Hyperspectral Imagery for Contamination Detection in Urban Areas, in Environmental Security and Ecoterrorism, 2011. Crossref

  7. Uss Mikhail L., Vozel Benoit, Lukin Vladimir V., Chehdi Kacem, Estimation of Variance and Spatial Correlation Width for Fine-Scale Measurement Error in Digital Elevation Model, IEEE Transactions on Geoscience and Remote Sensing, 58, 3, 2020. Crossref

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