Abstract
An inter-scale adaptive, data-driven threshold for image denoising via wavelet soft-thresholding is proposed. To get the optimal threshold, a Bayesian estimator is applied to the wavelet coefficients. The threshold is based on the accurate modeling of the distribution of wavelet coefficients using generalized Gaussian distribution (GGD), and the near exponential prior of the wavelet coefficients across scales. The new approach outperforms BayesShrink because it captures the statistical inter-scale property of wavelet coefficients, and is more adaptive to the data of each subband. Simulation results show that higher peak-signal-to-noise ratio can be obtained as compared to other thresholding methods for image denoising.
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Chen, Y., Lei, L., Ji, ZC., Sun, JF. (2007). Adaptive Wavelet Threshold for Image Denoising by Exploiting Inter-scale Dependency. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_87
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DOI: https://doi.org/10.1007/978-3-540-74171-8_87
Publisher Name: Springer, Berlin, Heidelberg
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