Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Tan, Xin, Lai, Shiming, Liu, Yu, Zhang, Maojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
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
_version_ 1782309056169377792
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
work_keys_str_mv AT tanxin greenchannelguidingdenoisingonbayerimage
AT laishiming greenchannelguidingdenoisingonbayerimage
AT liuyu greenchannelguidingdenoisingonbayerimage
AT zhangmaojun greenchannelguidingdenoisingonbayerimage