Abstract
Most two-dimensional methods for wavelet shrinkage are efficient for edge-preserving image denoising, but they suffer from poor rotation invariance. We address this problem by designing novel shrinkage rules that are derived from rotationally invariant nonlinear diffusion filters. The resulting Haar wavelet shrinkage methods are computationally inexpensive and they offer substantially improved rotation invariance.
This research was supported by the project Relations between Nonlinear Filters in Digital Image Processing within the DFG–Schwerpunktprogramm 1114: Mathematical Methods for Time Series Analysis and Digital Image Processing. This is gratefully acknowledged.
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Mrázek, P., Weickert, J. (2003). Rotationally Invariant Wavelet Shrinkage. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_21
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DOI: https://doi.org/10.1007/978-3-540-45243-0_21
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