Noise Filtration in Hyperspectral Images
| Autor: | V. V. Shipko |
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| Rok vydání: | 2020 |
| Předmět: |
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Computer science media_common.quotation_subject Gaussian ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 01 natural sciences Image (mathematics) law.invention 010309 optics symbols.namesake law 0103 physical sciences Redundancy (engineering) Contrast (vision) Electrical and Electronic Engineering Instrumentation Filtration media_common 010302 applied physics business.industry Hyperspectral imaging Pattern recognition Condensed Matter Physics Noise Computer Science::Computer Vision and Pattern Recognition symbols Artificial intelligence business |
| Zdroj: | Optoelectronics, Instrumentation and Data Processing. 56:19-27 |
| ISSN: | 1934-7944 8756-6990 |
| DOI: | 10.3103/s8756699020010033 |
| Popis: | An approach to filtration of hyperspectral images corrupted by the Gaussian additive noise is proposed. The approach is based on using the property of interchannel redundancy of such images. The developed algorithm of noise filtration allows maintaining the contour and brightness portraits of objects in individual components of the hyperspectral image, in contrast to algorithms of linear component-by-component and vector filtration, as well as the algorithm of averaging over a set of components. The numerical results obtained in the study testify to the advantage provided by interchannel gradient reconstruction in terms of the accuracy of recovery of hyperspectral image components corrupted by additive noise. The efficiency of the proposed approach is demonstrated by an example of processing of real hyperspectral images. |
| Databáze: | OpenAIRE |
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