Image denoising using local tangent space alignment

Abstract

We propose a novel image denoising approach, which is based on exploring an underlying (nonlinear) lowdimensional manifold. Using local tangent space alignment (LTSA), we ‘learn’ such a manifold, which approximates the image content effectively. The denoising is performed by minimizing a newly defined objective function, which is a sum of two terms: (a) the difference between the noisy image and the denoised image, (b) the distance from the image patch to the manifold. We extend the LTSA method from manifold learning to denoising. We introduce the local dimension concept that leads to adaptivity to different kind of image patches, e.g. flat patches having lower dimension. We also plug in a basic denoising stage to estimate the local coordinate more accurately. It is found that the proposed method is competitive: its performance surpasses the K-SVD denoising method.

Publication
Visual Communications and Image Processing 2010
Jianzhou Feng
Jianzhou Feng
PH.D Student
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.

Xiaoming Huo
Xiaoming Huo
Professor