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. © Springer-Verlag Berlin Heidelberg 2003.
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CITATION STYLE
Mrázek, P., & Weickert, J. (2003). Rotationally invariant wavelet shrinkage. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2781, 156–163. https://doi.org/10.1007/978-3-540-45243-0_21
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