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Optical Engineering

Image denoising by using nonseparable wavelet filters and two-dimensional principal component analysis
Author(s): Xinge You; Zaochao Bao; Chunfang Xing; Yiu-ming Cheung; Yuan Yan Tang; Maotang Li
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Paper Abstract

In this paper, we propose an image denoising method based on nonseparable wavelet filter banks and two-dimensional principal component analysis (2D-PCA). Conventional wavelet domain processing techniques are based on modifying the coefficients of separable wavelet transform of an image. In general, separable wavelet filters have limited capability of capturing the directional information. In contrast, nonseparable wavelet filters contain the basis elements oriented at a variety of directions and different filter banks capture the different directional features of an image. Furthermore, we identify the patterns from the noisy image by using the 2D-PCA. In comparison to the prevalent denoising algorithms, our proposed algorithm features no complex preprocessing. Furthermore, we can adjust the wavelet coefficients by a threshold according to the denoising results. We apply our proposed technique to some benchmark images with white noise. Experimental results show that our new technique achieves both good visual quality and a high peak signal-to-noise ratio for the denoised images.

Paper Details

Date Published: 1 October 2008
PDF: 11 pages
Opt. Eng. 47(10) 107002 doi: 10.1117/1.3002369
Published in: Optical Engineering Volume 47, Issue 10
Show Author Affiliations
Xinge You, Huazhong Univ. of Science and Technology (China)
Zaochao Bao, Huawei Technologies Co., Ltd. (China)
Chunfang Xing, Huawei Technologies Co., Ltd. (China)
Yiu-ming Cheung, Hong Kong Baptist Univ. (Hong Kong, China)
Yuan Yan Tang
Maotang Li, China Institute of Water Resources and Hydropower Research (China)


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