We propose a mixture of multiple linear models, also known as hybrid linear model, for a sparse representation of an image. This is a generalization of the conventional KarhunenLo...
object should be visible which were not visible in any of the images used to represent the object. So far these problems have been addressed by compression schemes [9] and restrict...
We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incohe...
Functional MRI (fMRI) has been widely accepted as a standard tool to study the function of brain. However, because of the limited temporal resolution of MR scanning, researchers h...
The fundamental principle underlying compressed sensing is that a signal, which is sparse under some basis representation, can be recovered from a small number of linear measuremen...