3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform
Journal of Mathematical Imaging and Vision
Compressed sensing of astronomical images: orthogonal wavelets domains
Proceedings of the 12th International Conference on Computer Systems and Technologies
Linear inverse problems with various noise models and mixed regularizations
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Compressive sensing for polyharmonic subdivision wavelets with applications to image analysis
Proceedings of the 13th International Conference on Computer Systems and Technologies
On MAP and MMSE estimators for the co-sparse analysis model
Digital Signal Processing
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This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics. A final chapter explores a paradigm shift in signal processing, showing that previous limits to information sampling and extraction can be overcome in very significant ways. Matlab and IDL code accompany these methods and applications to reproduce the experiments and illustrate the reasoning and methodology of the research available for download at the associated Web site.