Automatic parameter selection for a k-segments algorithm for computing principal curves
Pattern Recognition Letters
Spatial adaptive Bayesian wavelet threshold exploiting scale and space consistency
Multidimensional Systems and Signal Processing
Improved spatially adaptive MDL denoising of images using normalized maximum likelihood density
Image and Vision Computing
Support vector regression based image denoising
Image and Vision Computing
CoCo: coding cost for parameter-free outlier detection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Image and video denoising using adaptive dual-tree discrete wavelet packets
IEEE Transactions on Circuits and Systems for Video Technology
Wavelet-based CR image denoising by exploiting inner-scale dependency
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Document image analysis: issues, comparison of methods and remaining problems
Artificial Intelligence Review
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This paper presents a new spatially adaptive wavelet denoising method. Based on a doubly stochastic process model of wavelet coefficients, the method gives a new threshold, which varies spatially according to the variances of the coefficients, using the minimum description length (MDL) principle. The new threshold is not only easier to analyze since it is in a closed form, but also provides more facility for future compression than several other methods, almost without deteriorating mean square error risk.