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Green channel guiding denoising on bayer image.
Tan, Xin; Lai, Shiming; Liu, Yu; Zhang, Maojun.
Afiliação
  • Tan X; College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China.
  • Lai S; College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China.
  • Liu Y; College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China.
  • Zhang M; College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China.
ScientificWorldJournal ; 2014: 979081, 2014.
Article em En | MEDLINE | ID: mdl-24741370
ABSTRACT
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Interpretação de Imagem Assistida por Computador / Aumento da Imagem Idioma: En Revista: ScientificWorldJournal Assunto da revista: MEDICINA Ano de publicação: 2014 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Interpretação de Imagem Assistida por Computador / Aumento da Imagem Idioma: En Revista: ScientificWorldJournal Assunto da revista: MEDICINA Ano de publicação: 2014 Tipo de documento: Article País de afiliação: China