Optimized sampling distribution based on nonparametric learning for improved compressive sensing performance ?
A new learning approach for optimizing CS sampling distribution was introduced. The distribution is based on learning statistical properties of past measurements. Experiments performed with fluorescence microscopy and laser range measurements. Results consistently show significantly improved compressive sensing performance.
compressive sensing fluorescence microscopy laser range measurement compressed sampling sparse measurements data acquisition learned sampling distribution markov chain monte carlo (mcmc)