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4 August 2010 Image denoising using local tangent space alignment
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Proceedings Volume 7744, Visual Communications and Image Processing 2010; 774423 (2010) https://doi.org/10.1117/12.863472
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China
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.
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JianZhou Feng, Li Song, Xiaoming Huo, XiaoKang Yang, and Wenjun Zhang "Image denoising using local tangent space alignment", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 774423 (4 August 2010); https://doi.org/10.1117/12.863472
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