Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Relations Between Regularization and Diffusion Filtering
Journal of Mathematical Imaging and Vision
Combining total variation and wavelet packet approaches for image deblurring
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
De-noising by soft-thresholding
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
Diffusion-Inspired Shrinkage Functions and Stability Results for Wavelet Denoising
International Journal of Computer Vision
Wavelet-based diffusion approaches for signal denoising
Signal Processing
Edge-preserving wavelet thresholding for image denoising
Journal of Computational and Applied Mathematics
Correspondences between wavelet shrinkage and nonlinear diffusion
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
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Soft wavelet shrinkage and total variation (TV) denoising are two frequently used techniques for denoising signals and images, while preserving their discontinuities. In this paper we show that - under specific circumstances - both methods are equivalent. First we prove that 1-D Haar wavelet shrinkage on a single scale is equivalent to a single step of TV diffusion or regularisation of two-pixel pairs. Afterwards we show that wavelet shrinkage on multiple scales can be regarded as a single step diffusion filtering or regularisation of the Laplacian pyramid of the signal.