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With the booming of energy-limited video devices ranging from visual sensors to camera cell phones, distributed compressed video sensing (DCVS) has developed as one of the efficient solutions that guarantee low complexity video encoding as well as acceptable recovery quality. In this paper, a noise-resilient DCVS scheme is proposed using(More)
Multiple nodes sensing the common target is the most popular application of the Wireless Sensor Networks (WSNs). Pure distributed compression of multiple correlated sources has been discussed much in the related literature, while taking the noisy communication channels into account is more suitable for the actual scenario. In this paper, a practical(More)
Many practical compressible signals like image signals or the networked data in wireless sensor networks have non-uniform support distribution in their sparse representation domain. Utilizing this prior information, a novel compressed sensing (CS) scheme with unequal protection capability is proposed in this paper by introducing a windowing strategy called(More)
Distributed compressive sensing (DCS) is a new technique that provides a low-complexity sub-Nyquist signal acquisition and reconstruction via a small number of random linear projections. In this paper, we propose sparse filter correlation model (SFCM) to exploit the correlations among successive video frames under the framework of distributed compressive(More)
Distributed Compressed Video Sensing (DCVS) has developed as one of the efficient solutions that guarantee low complexity video compression. In this paper, a novel DCVS algorithm with unequal protection of the video signal's elements is proposed. The new algorithm utilizes not only the sparsity and probability distribution of the video signal but also its(More)