A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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SIAM Journal on Numerical Analysis
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I
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In this paper we present a novel wavelet based method for edge preserving noise reduction. In contrast to most common methods, the algorithm introduced here does not work on single input data. It takes two or more spatially identical images, which are both impaired by noise. Assuming the statistical independence of noise in the different images, correlation computations can be used in order to preserve structures while reducing noise. Different methods for correlation analysis have been investigated, on the one hand based directly on the original input images and on the other hand taking into account the wavelet representation of the input data. The presented approach proves to be suited for the application in computed tomography, where high noise reduction rates of approximately 50% can be achieved without loss of structure information.