A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the goal of sensory coding?
Neural Computation
Multi-Noise Removal from Images by Wavelet-Based Bayesian Estimator
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Wavelet-based Bayesian estimator for Poisson noise removal from images
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
De-noising by soft-thresholding
IEEE Transactions on Information Theory
MIMO UWB-IR noncoherent transceiver with Poisson wireless models
ISWPC'09 Proceedings of the 4th international conference on Wireless pervasive computing
Behaviors of MIMO UWB-IR transceiver for statistical wireless channels
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 1
Hi-index | 0.00 |
Almost every digital image is unavoidably contaminated by various noise sources. In our previous paper, we focused on Gaussian and Poisson noises. Unlike additive Gaussian noise, Poisson noise is signal-dependent and separating signal from noise is a difficult task. A wavelet-based maximum likelihood method for Bayesian estimator that recovers the signal component of the wavelet coefficients in original images by using an alpha-stable signal prior distribution is demonstrated to the discussed noise removal. Current paper is to extend out previous results to more complex cases that noises comprised of compound Poisson, Gaussian, and impulse noises via Lévy process analysis. As an example, an improved Bayesian estimator that is a natural extension of other wavelet denoising via a colour image is presented to illustrate our discussion.