Removal of Gaussian Noise from Degraded Images in Wavelet Domain

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  • Li Yeqiu
    Graduate School of Science and Technology, Chiba University
  • Lu Jianming
    Graduate School of Science and Technology, Chiba University
  • Wang Ling
    Graduate School of Science and Technology, Chiba University
  • Yahagi Takakshi
    Graduate School of Science and Technology, Chiba University

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  • ウェーブレット領域における劣化画像のガウス性雑音除去
  • ウェーブレット リョウイキ ニ オケル レッカ ガゾウ ノ ガウスセイ ザツオン ジョキョ

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Abstract

The observed images are often corrupted by Gaussian noise. If the image is embedded in small-amplitude Gaussian noise, the noise can be removed by applying Wiener filter. Recently, the BayesShrink wavelet method has attracted considerable attention as a denoising technique. In this paper, we propose a method for removal of Gaussian noise of large amplitude as well as of small one, which can not be removed only by exploiting the BayesShrink wavelet method. Our approach is a combination of the BayesShrink wavelet method with the directional adaptive center weighted median filter. Applying the proposed method to an image corrupted by large-amplitude Gaussian noise, a clean image can be obtained.

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