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Image Denoising

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Sparse and Redundant Representations
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Abstract

Images often contain noise, which may arise due to sensor imperfection, poor illumination, or communication errors. Removing such noise is of great benefit in many applications, and this may explain the vast interest in this problem and its solution. However, the importance of the image denoising problem goes beyond the evident applications it serves. Being the simplest possible inverse problem, it provides a convenient platform over which image processing ideas and techniques can be tested and perfected. Indeed, numerous contributions in the past 50 years or so address this problem from many and diverse points of view. Statistical estimators of all sorts, spatial adaptive filters, stochastic analysis, partial differential equations, transform-domain methods, splines and other approximation theory methods, morphological analysis, differential geometry, order statistics, and more, are some of the many directions and tools explored in studying this problem.

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Correspondence to Michael Elad .

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Elad, M. (2010). Image Denoising. In: Sparse and Redundant Representations. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7011-4_14

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