Zusammenfassung
By this contribution we tackle the challenge of denoising MRI image data while preserving fine structures. Log-Gabor wavelets offer a good compromise between spatial and spectral resolution, thus allowing to extract the local phase at all image voxels. Shrinking only the complex-valued wavelet response vectors at different scales and orientations leaves the essential phase information undistorted. We propose an adaptive shrinking threshold based on supervoxels and also based on the amount of texture in a particular image region. Therefore, the proposed adaptive 3D technique preserves fine structures and outperforms existing methods in terms of peak signal-to-noise ratio (PSNR)/structural similarity (SSIM) in cases of elevated noise perturbation.
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Morariu, C.A., Eckhardt, A., Terheiden, T., Landgräber, S., Jäger, M., Pauli, J. (2018). 3D Adaptive Wavelet Shrinkage Denoising while Preserving Fine Structures. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_95
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DOI: https://doi.org/10.1007/978-3-662-56537-7_95
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