In information hiding scheme, the security and robustness of watermarking are two important performances. Based on the design of secure watermarking signal, this paper proposes a robust video information hiding solution for protecting fingerprint content. In our proposed method, construction of fingerprint watermarking signal from the compressed sensing (CS) measurements relies on the knowledge...

We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. The basic idea behind CS is that a signal or image, unknown but supposed to be compressible by a known transform, (eg. wavelet or Fourier), can be subjected to fewer measurements than the nominal number of pixels, and yet be accurately reconstructed. The samples are nonadaptive and measure ‘random...

Compressed sensing is a recently developed technique that exploits the sparsity of naturally occurring signals and images to reduce the volume of the data using less number of samples, computing the sparsity of the signal. In the traditional/conventional approaches the images are acquired and compressed, where as compressed sensing aims to acquire the “compressed signals” with few numbers of sa...

based on the compressed sensing theory, if a signal is sparse in a suitable space, by using the optimization methods, signal could be accurately reconstructed from measurements that are significantly less than the theoretical shannon requirements. the sparse representation may exist for the signal and it is not available for the noise; this could be used to distinguish these two. on the other h...

CS, as a new compression paradigm, relies on three main requirements: sparsity representation, incoherence measurement, and nonlinear reconstruction, which pertain to the signals of interest, the encoding modality, and the decoding method, respectively. The main goal of the CS is to accurately reconstruct a high dimensional sparse vector using a small number of linear measurements. As in wirele...

Much attention has recently been paid to direction of arrival (DOA) estimation using compressed sensing (CS) techniques, which are sparse signal reconstruction methods. In our previous study, we developed a method for estimating the DOAs of multi-band signals that uses CS processing and that is based on the assumption that incident signals have the same complex amplitudes in all the bands. That...

The `1 tracker obtains robustness by seeking a sparse representation of the tracking object via `1 norm minimization [1]. However, the high computational complexity involved in the `1 tracker restricts its further applications in real time processing scenario. Hence we propose a Real Time Compressed Sensing Tracking (RTCST) by exploiting the signal recovery power of Compressed Sensing (CS). Dim...

Reducing acquisition time is of fundamental importance in various imaging modalities. The concept of variable density sampling provides a nice framework to address this issue. It was justified recently from a theoretical point of view in the compressed sensing (CS) literature. Unfortunately, the sampling schemes suggested by current CS theories may not be relevant since they do not take the acq...

Compressed sensing (CS) is a rising focus in recent years for its simultaneous sampling and compression of sparse signals. Speech signals can be considered approximately sparse or compressible in some domains for natural characteristics. Thus, it has great prospect to apply compressed sensing to speech signals. This paper is involved in three aspects. Firstly, the sparsity and sparsifying matri...