Abstract
In this paper, we develop a new wavelet domain statistical model for the removal of stationary noise in images. The new model is a combination of local linear projections onto bases of Principal Components, that perform a dimension reduction of the spatial neighbourhood, while avoiding the ”curse of dimensionality”. The models obtained after projection consist of a low dimensional Gaussian Scale Mixtures with a reduced number of parameters. The results show that this technique yields a significant improvement in denoising performance when using larger spatial windows, especially on images with highly structured patterns, like textures.
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Goossens, B., Pižurica, A., Philips, W. (2007). Noise Removal from Images by Projecting onto Bases of Principal Components. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_17
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DOI: https://doi.org/10.1007/978-3-540-74607-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74606-5
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