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IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet-based signal de-noising via simple singularities approximation
Signal Processing
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
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SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
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IEEE Transactions on Signal Processing
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IEEE Transactions on Image Processing
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IEEE Transactions on Image Processing
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IEEE Transactions on Image Processing
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IEEE Transactions on Image Processing
Image Restoration Using Space-Variant Gaussian Scale Mixtures in Overcomplete Pyramids
IEEE Transactions on Image Processing
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This paper presents a de-noising method that recognizes similarities in the image through the time scale behaviour of wavelet coefficients. Wavelet details are represented as linear combination of predefined atoms whose center of mass traces trajectories in the time scale plane (from fine to coarse scale). These trajectories are the solution of a proper ordinary differential equation and characterize atoms corresponding to groups of not isolated singularities in the signal. The distances among atoms, the ratio of their amplitudes and the difference of their decay along scales are the parameters to use for defining similarities in the image. Experimental results show the potentialities of the method in terms of visual quality and mean square error, reaching the most powerful and recent de-noising schemes.