Sparse Representations for Signal Processing

Spring 2018

This is a graduate level course that will focus on foundations of the general framework of sparse representations (a foundational tool for applications such as compressive sensing, denoising and classification), theory of multi-resolution analysis frameworks (wavelet systems in 1D, 2D and 3D, curvelets, ridgelets, contourlets), and structured sparsity. Applications to image denoising, image compression and image analysis will be introduced. The course will have a rigorous theoretical component and a hands-on project component where students will apply these techniques to a real-world image analysis problem.

More details (Syllabus, schedule etc.) will be posted to this page later in Fall.


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