Local Spectral Component Decomposition for Multi-Channel Image Denoising

Mia Rizkinia, Tatsuya Baba, Keiichiro Shirai, Masahiro Okuda

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


We propose a method for local spectral component decomposition based on the line feature of local distribution. Our aim is to reduce noise on multi-channel images by exploiting the linear correlation in the spectral domain of a local region. We first calculate a linear feature over the spectral components of an M -channel image, which we call the spectral line, and then, using the line, we decompose the image into three components: a single M -channel image and two gray-scale images. By virtue of the decomposition, the noise is concentrated on the two images, and thus our algorithm needs to denoise only the two gray-scale images, regardless of the number of the channels. As a result, image deterioration due to the imbalance of the spectral component correlation can be avoided. The experiment shows that our method improves image quality with less deterioration while preserving vivid contrast. Our method is especially effective for hyperspectral images. The experimental results demonstrate that our proposed method can compete with the other state-of-the-art denoising methods.

Original languageEnglish
Article number7463552
Pages (from-to)3208-3218
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number7
Publication statusPublished - Jul 2016


  • Spectral line
  • denoising
  • hyperspectral image
  • local spectral component decomposition


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