Many areas in signal processing have benefited from the emergence of compressed sensing and sparse reconstruction methods, one of which is magnetic resonance imaging (MRI). Recent studies showed that MRI acquisition can be highly accelerated with the joint use of compressed sensing and parallel imaging methods. It is also suggested that dynamic MRI can be further improved by making use of temporal correlations. Although methods using motion compensation has been proposed to exploit temporal dependence, most of these require reference frames and/or a sub-portion of k-space to be fully sampled. In this paper we propose a new approach to exploit the motion information during compressed sensing reconstruction without any requirement for reference frames, modeled motion or a specific sampling pattern on the k-space measurements.