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
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Journal of Scientific Computing
A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
Analysis of polynomial FM signals corrupted by heavy-tailed noise
Signal Processing
Image quality assessment using the joint spatial/spatial-frequency representation
EURASIP Journal on Applied Signal Processing
Modified switching median filter for impulse noise removal
Signal Processing
Video frames reconstruction based on time-frequency analysis and Hermite projection method
EURASIP Journal on Advances in Signal Processing - Special issue on time-frequency analysis and its applications to multimedia signals
A method for time-frequency analysis
IEEE Transactions on Signal Processing
Robust L-estimation based forms of signal transforms and time-frequency representations
IEEE Transactions on Signal Processing
Image denoising: a nonlinear robust statistical approach
IEEE Transactions on Signal Processing
Space/spatial-frequency analysis based filtering
IEEE Transactions on Signal Processing
Wigner distribution of noisy signals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Automatica (Journal of IFAC)
Minimax description length for signal denoising and optimized representation
IEEE Transactions on Information Theory
Adaptive alpha-trimmed mean filters under deviations from assumed noise model
IEEE Transactions on Image Processing
Multimedia Signals and Systems
Multimedia Signals and Systems
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Most of the commonly used stationary filtering techniques, performed either in the spatial or frequency domains, fail to produce good results for noisy signals with fast varying non-stationary structures. The filtering results could be improved by using space/spatial-frequency based non-stationary filters. Hence, a robust approach to space/spatial-frequency analysis of two-dimensional noisy signals is proposed in this paper. It is based on the two-dimensional L-estimate forms of the short-time Fourier transform, the spectrogram and the S-method. The proposed space/spatial-frequency distributions are used to define the L-estimate space-varying filtering procedure. It is designed for denoising of 2D non-stationary signals affected by the strong impulsive or mixed heavy-tailed and Gaussian noise. The efficiency of the proposed procedure is tested on the examples with interferogram-like images, textures and satellite images.