On the determination of optimal tuning parameters for a space-variant LASSO problem using geometric and convex analysis techniques

Abstract

Compressed Sensing (CS) comprises a wide range of theoretical and applied techniques to recover signals given a partial knowledge of their coefficients. It finds its applications in several fields, such as mathematics, physics, engineering, and many medical sciences, to name a few, cf. [17,7,13,25,33,38,36,56,57]. Driven by our interest in the mathematics behind Magnetic Resonance Imaging (MRI) and Compressed Sensing (CS), we use convex analysis techniques to determine analytically the optimal tuning parameters of the space-variant LASSO with voxel-wise weighting, under assumptions on the fidelity term, either on the sign of its gradient or orthogonality-like conditions on its matrix. Finally, we conclude conjecturing what the explicit form of optimal parameters should be in the most general setting (hypotheses-free) of the space-variant LASSO.