Abstract
We combine the techniques of the complex wavelet transform and Markov random fields (MRF) model to restore natural images in white Gaussian noise. The complex wavelet transform outperforms the standard real wavelet transform in the sense of shift-invariance, directionality and complexity. The prior MRF model is used to exploit the clustering property of the wavelet transform, which can effectively remove annoying pointlike artifacts associated with standard wavelet denoising methods. Our experimental results significantly outperform those using standard wavelet transforms and are comparable to those from overcomplete wavelet transforms and MRFs, but with much less complexity.
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© 2005 Springer-Verlag Berlin Heidelberg
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Jin, F., Fieguth, P., Winger, L. (2005). Image Denoising Using Complex Wavelets and Markov Prior Models. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_10
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DOI: https://doi.org/10.1007/11559573_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29069-8
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