On the reconstruction of sequences of sparse signals – The Weighted-CS

In this paper, we study the problem of recursively reconstructing time sequences of sparse signals, where sparsity changes smoothly with time. The idea is to use the signal/image of the previous time instance to extract an estimated probability model for the signal/image of interest, and then use this model to guide the reconstruction process. We examine and illustrate the performance of our approach, “Weighted-CS”, with both synthetic and real medical signals/images. It is shown that we can achieve significant performance improvement, using fewer number of samples, compared to other state-of-art Compressive Sensing methods.

► We study the problem of reconstructing time sequences of sparse signals.
► We use the signal of the previous time instance to extract a probability model.
► This priori knowledge is used in a weighted l_1 approach to guide the reconstruction.
► We achieved significant performance improvement, using fewer number of samples.