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JMLR
2010

Locally Linear Denoising on Image Manifolds

11 years 1 months ago
Locally Linear Denoising on Image Manifolds
We study the problem of image denoising where images are assumed to be samples from low dimensional (sub)manifolds. We propose the algorithm of locally linear denoising. The algorithm approximates manifolds with locally linear patches by constructing nearest neighbor graphs. Each image is then locally denoised within its neighborhoods. A global optimal denoising result is then identified by aligning those local estimates. The algorithm has a closed-form solution that is efficient to compute. We evaluated and compared the algorithm to alternative methods on two image data sets. We demonstrated the effectiveness of the proposed algorithm, which yields visually appealing denoising results, incurs smaller reconstruction errors and results in lower error rates when the denoised data are used in supervised learning tasks.
Dian Gong, Fei Sha, Gérard G. Medioni
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Dian Gong, Fei Sha, Gérard G. Medioni
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