Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal
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Research Article Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal 1 Karen Egiazarian,2 Vladimir V. Lukin,3 Nikolay N. Ponomarenko,3 and Oleg V. Tsymbal4 ¨ Rus¸en Oktem,
˙ and Electronics Engineering Department, Atılım University, Kızılcas¸ar K¨oy¨u, 06836 Incek, Ankara, Turkey of Signal Processing, Tampere University of Technology, 33101 Tampere, Finland 3 Department of Receivers, Transmitters and Signal Processing, National Aerospace University, 17 Chkalova Street, 61070 Kharkov, Ukraine 4 Kalmykov Center for Radiophysical Sensing of Earth, 12 Ak. Proskury Street, 61085 Kharkov, Ukraine 1 Electrical 2 Institute
Received 13 October 2006; Revised 21 March 2007; Accepted 13 May 2007 Recommended by Stephen Marshall This work addresses the problem of signal-dependent noise removal in images. An adaptive nonlinear filtering approach in the orthogonal transform domain is proposed and analyzed for several typical noise environments in the DCT domain. Being applied locally, that is, within a window of small support, DCT is expected to approximate the Karhunen-Loeve decorrelating transform, which enables effective suppression of noise components. The detail preservation ability of the filter allowing not to destroy any useful content in images is especially emphasized and considered. A local adaptive DCT filtering for the two cases, when signaldependent noise can be and cannot be mapped into additive uncorrelated noise with homomorphic transform, is formulated. Although the main issue is signal-dependent and pure multiplicative noise, the proposed filtering approach is also found to be competing with the state-of-the-art methods on pure additive noise corrupted images. ¨ Copyright © 2007 Rus¸en Oktem et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Digital images are often degraded by noise, due to the imperfection of the acquisition system or the conditions during the acquisition. Noise decreases the perceptual quality by masking significant information, and also degrades performance of any processing applied over the acquired image. Hence, image prefiltering is a common operation used in order to improve analysis and interpretation of remote sensing, broadcast transmission, optical scanning, and other vision data [1, 2]. Till now a great number of different image filtering techniques have been designed including nonlinear nonadaptive and adaptive filters [3, 4], transform-based methods [5–11], techniques based on independent component analysis (ICA), and principal component analysis (PCA) [12, 13], and so forth. These techniques have different advantages and drawbacks thoroughly discussed in [3, 4, 14], and other references. The application areas and conditions for which the use of these filters can be the most beneficial and expedient depend on the filter properties, noise statistical characteristics, and the priorit
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