A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial splines and wavelets: a signal processing perspective
Wavelets: a tutorial in theory and applications
Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
Improved multilook technique applied to SAR images
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Derivation of centralized and distributed filters using covariance information
Computational Statistics & Data Analysis
Fast reduction of speckle noise in real ultrasound images
Signal Processing
Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images
Digital Signal Processing
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Synthetic aperture sonar (SAS) is actively used in sea bed imagery. Indeed high resolution images provided by SAS are of great interest, especially for the detection, localization and eventually classification of objects lying on sea bed. SAS images are highly corrupted by a granular multiplicative noise, called speckle noise which reduces spatial and radiometric resolutions. The purpose of this article is to present a new adaptive processing that allows image filtering, for both the additive and multiplicative noise case. This new process is based on the marriage between a multi-resolution transformation and a filtering method. The filtering technique used here is based on the two-dimensional stochastic matched filtering method, which maximizes the signal-to-noise ratio after processing and minimizes mean square error between the signal's approximation and the original one. Results obtained on real SAS data are presented and compared with those obtained using classical processing.