
Proceedings Paper
Image denoising using locally learned dictionariesFormat | Member Price | Non-Member Price |
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Paper Abstract
In this paper we discuss a novel patch-based framework for image denoising through local geometric representations
of an image. We learn local data adaptive bases that best capture the underlying geometric information
from noisy image patches. To do so we first identify regions of similar structure in the given image and group
them together. This is done by the use of meaningful features in the form of local kernels that capture similarities
between pixels in a neighborhood. We then learn an informative basis (called a dictionary) for each
cluster that best describes the patches in the cluster. Such a data representation can be achieved by performing
a simple principal component analysis (PCA) on the member patches of each cluster. The number of principal
components to consider in a particular cluster is dictated by the underlying geometry captured by the cluster
and the strength of the corrupting noise. Once a dictionary is defined for a cluster, each patch in the cluster is
denoised by expressing it as a linear combination of the dictionary elements. The coefficients of such a linear
combination for any particular patch is determined in a regression framework using the local dictionary for the
cluster. Each step of our method is well motivated and is shown to minimize some cost function. We then
present an iterative extension of our algorithm that results in further performance gain. We validate our method
through experiments with simulated as well as real noisy images. These indicate that our method is able to
produce results that are quantitatively and qualitatively comparable to those obtained by some of the recently
proposed state of the art denoising techniques.
Paper Details
Date Published: 2 February 2009
PDF: 10 pages
Proc. SPIE 7246, Computational Imaging VII, 72460V (2 February 2009); doi: 10.1117/12.810486
Published in SPIE Proceedings Vol. 7246:
Computational Imaging VII
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)
PDF: 10 pages
Proc. SPIE 7246, Computational Imaging VII, 72460V (2 February 2009); doi: 10.1117/12.810486
Show Author Affiliations
Priyam Chatterjee, Univ. of California, Santa Cruz (United States)
Peyman Milanfar, Univ. of California, Santa Cruz (United States)
Published in SPIE Proceedings Vol. 7246:
Computational Imaging VII
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)
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