Employing correlation among images for improved reconstruction in compressive sensing is
a conceptually attractive idea, although developing efficient modeling strategies and …

The existing video compressed sensing (CS) algorithms for inconsistent sampling ignore the
joint correlations of video signals in space and time, and their reconstruction quality and …

In order to merge the advantages of the traditional compressed sensing (CS) methodology
and the data-driven deep network scheme, this paper proposes a physical model-driven …

Abstract The Compressive Video Sensing (CVS) is a useful technology for wireless systems
requiring simple encoders but handling more complex decoders, and its rate-distortion …

High reconstructed performance compressed video sensing (CVS) with low computational
complexity and memory requirement is very challenging. In order to reconstruct the high …

Image recovery from compressive sensing (CS) measurement data, especially noisy data
has always been challenging due to its implicit ill-posed nature, thus, to seek a domain …

In this paper, it is proposed the directional estimation model on the overcomplete dictionary,
which bridges the compressed measurements of the image blocks and the directional …

In compressed sensing (CS), one seeks to down-sample some high-dimensional signals
and recover them accurately by exploiting the sparsity of the signals. However, the …

Abstract Compressed Video Sensing (CVS) has great significance to the scenarios with a
resource-deprived video acquisition side. Reconstruction algorithm is the key technique in …

Multi-modal sensing is increasingly becoming important in a number of applications,
providing new capabilities and processing challenges. In this paper, we explore the benefit …