Non-Gaussian modeling and multiscale Bayesian processing for various signal modalities
- Multiscale methods have revolutionized signal processing. Wavelets are particularly suited for representing and yielding sparse and structured representations of piecewise smooth signals such as images.
- We accurately characterized the sparsity of the wavelet coefficients by using alpha-stable statistical modeling.
- We designed new Bayesian algorithms based on the more accurate models. We tested and validated our processing philosophy to various application domains including.
- image retrieval, fusion, and watermarking
- SAR image denoising and autofocus
- underwater acoustic signal classification
- biomedical (ultrasound, microarray, miRNA) signal enhancement and classification
