Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional signal and image processing
Two-dimensional signal and image processing
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
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiotemporally Adaptive Estimation and Segmenation of OF-Fields
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Wiener channel smoothing: robust wiener filtering of images
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
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This contribution presents a variation of the Wiener filter criterion, i.e. minimizing the mean squared error, by combining it with the main principle of normalized convolution, i.e. the introduction of prior information in the filter process via the certainty map. Thus, we are able to optimize a filter according to the signal and noise characteristics while preserving edges in images. In spite of its low computational costs the proposed filter schemes outperforms state of the art filter methods working also in the spatial domain. Furthermore, the Wiener filter paradigm is extended from scalar valued data to tensor valued data.