MMSE Filtering of generalised signal-dependent noise in spatial and shift-invariant wavelet domains
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
A hybrid multi-scale model for thyroid nodule boundary detection on ultrasound images
Computer Methods and Programs in Biomedicine
A versatile technique for visual enhancement of medical ultrasound images
Digital Signal Processing
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering of interferometric SAR phase images as a fuzzy matching-pursuit blind estimation
EURASIP Journal on Applied Signal Processing
SAR image regularization with fast approximate discrete minimization
IEEE Transactions on Image Processing
Wavelet-based SAR image despeckling and information extraction, using particle filter
IEEE Transactions on Image Processing
Nonlocal means-based speckle filtering for ultrasound images
IEEE Transactions on Image Processing
SIP'10 Proceedings of the 9th WSEAS international conference on Signal processing
Efficient time and frequency methods for sampling filter functions
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
A Bayesian approach of wavelet based image denoising in a hyperanalytic multi-wavelet context
WSEAS Transactions on Signal Processing
SAR speckle reduction based on undecimated tree-structured wavelet transform
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images
Digital Signal Processing
Hi-index | 0.01 |
Synthetic aperture radar (SAR) images are disturbed by a multiplicative noise depending on the signal (the ground reflectivity) due to the radar wave coherence. Images have a strong variability from one pixel to another reducing essentially the efficiency of the algorithms of detection and classification. We propose to filter this noise with a multiresolution analysis of the image. The wavelet coefficient of the reflectivity is estimated with a Bayesian model, maximizing the a posteriori probability density function. The different probability density function are modeled with the Pearson system of distributions. The resulting filter combines the classical adaptive approach with wavelet decomposition where the local variance of high-frequency images is used in order to segment and filter wavelet coefficients