In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing. In video compressive sensing one frame is acquired using a set of coded masks (sensing matrix) from which a number of video frames is reconstructed, equal to the number of coded masks. The proposed framework is an end-to-end model where the sensing matrix is traine...

In this paper, the design of universal compressive sensing filter based on normal filters including the lowpass, highpass, bandpass, and bandstop filters with different cutoff frequencies (or bandwidth) has been developed to enable signal acquisition with sub-Nyquist sampling. Moreover, to control flexibly the size and the coherence of the compressive sensing filter, as an example, the microstr...

Compressive sensing is a new data acquisition technique that aims to measure sparse and compressible signals at close to their intrinsic information rate rather than their Nyquist rate. Recent results in compressive sensing show that a sparse or compressible signal can be reconstructed from very few measurements with an incoherent, and even randomly generated, dictionary. To date the hardware i...

An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with compressive sensing of large data, which is typical to hyperspectral imaging. The syste...

A new color image encryption algorithm combining compressive sensing with Arnold transform is proposed, which can encrypt the color image into a gray image. Considering the dimensional reduction and random projection of compressive sensing, we utilize compressive sensing to encrypt and compress the three color components of color image simultaneously. The three encrypted and compressed color co...

Traditionally image reconstruction is done by performing Fast Fourier Transform (FFT). But recently there has been growing interest in using compressive sensing (CS) to perform image reconstruction.In compressive sensing, the main property of signal-Sparsity is explored for reconstruction purposes.In this paper, for image reconstruction, various optimization techniques like L1 optimization, Tot...

In this paper, we describe an image watermarking scheme using compressive sensing for detection of image tampering. We exploit dimensionality reduction and inherent computational security offered by compressive sensing. The frequency band containing the high energy is used to compute the hash and then its pseudo-random projections are obtained. These projections are embedded as a watermark into...

Three-dimensional (3D) object tomography from a two-dimensional recorded hologram is a process of high-dimensional data inference from undersampled data. As such, recently, techniques developed in the field of compressive sensing and sparse representation have been applied for this task. While many applications of compressive sensing for tomography from digital holograms have been demonstrated ...

Wireless sensor networks consist of a network in which node are spatially distributed. These sensor nodes communicates with each other for transferring data from one node to another. These sensor nodes while transfer of data consumes energy. So the energy consumption is high in wireless sensor networks. To reduce the energy consumption the compressive sensing method is given known as compressiv...