Image denoising based on local adaptive multi-scale wavelet least squares support vector regression (MWLS_SVR)

Dingxue Wu; Daiqiang Peng; Jinwen Tian

doi:10.1117/12.747962

15 November 2007 Image denoising based on local adaptive multi-scale wavelet least squares support vector regression (MWLS_SVR)

Dingxue Wu, Daiqiang Peng, Jinwen Tian

Author Affiliations +

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 678618 (2007) https://doi.org/10.1117/12.747962
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive multi-scale wavelet least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of the edges and details.

Citation Download Citation

Dingxue Wu, Daiqiang Peng, and Jinwen Tian "Image denoising based on local adaptive multi-scale wavelet least squares support vector regression (MWLS_SVR)", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 678618 (15 November 2007); https://doi.org/10.1117/12.747962

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