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Green Channel Guiding Denoising on Bayer Image
Denoising is an indispensable function for digital cameras. In respect that noise is diffused during the demosaicking, the denoising ought to work directly on bayer data. The difficulty of denoising on bayer image is the interlaced mosaic pattern of red, green, and blue. Guided filter is a novel tim...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3967728/ https://www.ncbi.nlm.nih.gov/pubmed/24741370 http://dx.doi.org/10.1155/2014/979081 |
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author | Tan, Xin Lai, Shiming Liu, Yu Zhang, Maojun |
author_facet | Tan, Xin Lai, Shiming Liu, Yu Zhang, Maojun |
author_sort | Tan, Xin |
collection | PubMed |
description | Denoising is an indispensable function for digital cameras. In respect that noise is diffused during the demosaicking, the denoising ought to work directly on bayer data. The difficulty of denoising on bayer image is the interlaced mosaic pattern of red, green, and blue. Guided filter is a novel time efficient explicit filter kernel which can incorporate additional information from the guidance image, but it is still not applied for bayer image. In this work, we observe that the green channel of bayer mode is higher in both sampling rate and Signal-to-Noise Ratio (SNR) than the red and blue ones. Therefore the green channel can be used to guide denoising. This kind of guidance integrates the different color channels together. Experiments on both actual and simulated bayer images indicate that green channel acts well as the guidance signal, and the proposed method is competitive with other popular filter kernel denoising methods. |
format | Online Article Text |
id | pubmed-3967728 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-39677282014-04-16 Green Channel Guiding Denoising on Bayer Image Tan, Xin Lai, Shiming Liu, Yu Zhang, Maojun ScientificWorldJournal Research Article Denoising is an indispensable function for digital cameras. In respect that noise is diffused during the demosaicking, the denoising ought to work directly on bayer data. The difficulty of denoising on bayer image is the interlaced mosaic pattern of red, green, and blue. Guided filter is a novel time efficient explicit filter kernel which can incorporate additional information from the guidance image, but it is still not applied for bayer image. In this work, we observe that the green channel of bayer mode is higher in both sampling rate and Signal-to-Noise Ratio (SNR) than the red and blue ones. Therefore the green channel can be used to guide denoising. This kind of guidance integrates the different color channels together. Experiments on both actual and simulated bayer images indicate that green channel acts well as the guidance signal, and the proposed method is competitive with other popular filter kernel denoising methods. Hindawi Publishing Corporation 2014-03-11 /pmc/articles/PMC3967728/ /pubmed/24741370 http://dx.doi.org/10.1155/2014/979081 Text en Copyright © 2014 Xin Tan et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Tan, Xin Lai, Shiming Liu, Yu Zhang, Maojun Green Channel Guiding Denoising on Bayer Image |
title | Green Channel Guiding Denoising on Bayer Image |
title_full | Green Channel Guiding Denoising on Bayer Image |
title_fullStr | Green Channel Guiding Denoising on Bayer Image |
title_full_unstemmed | Green Channel Guiding Denoising on Bayer Image |
title_short | Green Channel Guiding Denoising on Bayer Image |
title_sort | green channel guiding denoising on bayer image |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3967728/ https://www.ncbi.nlm.nih.gov/pubmed/24741370 http://dx.doi.org/10.1155/2014/979081 |
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