—In this paper, we propose a lensless compressive sensing imaging architecture. The architecture consists of two components, an aperture assembly and a sensor. No lens is used. The aperture assembly consists of a two dimensional array of aperture elements. The transmittance of each aperture element is independently controllable. The sensor is a single detection element, such as a single photo-c...

This paper presents a novel approach to capture light field in camera arrays based on the compressive sensing framework. Light fields are captured by a linear array of cameras with overlapping field of view. In this work, we design a redundant dictionary to exploit cross-cameras correlated structures in order to sparsely represent cameras image. We show experimentally that the projection of com...

Distributed Compressive Sensing (DCS) [1] improves the signal recovery performance of multi signal ensembles by exploiting both intraand inter-signal correlation and sparsity structure. However, the existing DCS was proposed for a very limited ensemble of signals that has single common information [1]. In this paper, we propose a generalized DCS (GDCS) which can improve sparse signal detection ...

We study the average distortion introduced by scalar, vector, and entropy coded quantization of compressive sensing (CS) measurements. The asymptotic behavior of the underlying quantization schemes is either quantified exactly or characterized via bounds. We adapt two benchmark CS reconstruction algorithms to accommodate quantization errors, and empirically demonstrate that these methods signif...

We consider the reconstruction problem in compressed sensing in which the observations are recorded in a finite number of bits. They may thus contain quantization errors (from being rounded to the nearest representable value) and saturation errors (from being outside the range of representable values). Our formulation has an objective of weighted l2-l1 type, along with constraints that account ...

We present an end-to-end image compression system based on compressive sensing. The presented system integrates the conventional scheme of compressive sampling and reconstruction with quantization and entropy coding. The compression performance, in terms of decoded image quality versus data rate, is shown to be comparable with JPEG and significantly better at the low rate range. We study the pa...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Compressive sensing (CS) is a fast growing area of research. It neglects the extravagant acquisition process by measuring lesser values to reconstruct the image or signal. Compressive sensing is adopted successfully in various fields of image proce...

A low-power asynchronous compressive sensing scheme is proposed for radar. Power and design cost are optimized by combining asynchronous sampling and compressive sensing which decreases both the duty cycle of front-end circuits and the data volume of the ADC interface. In the signal reconstruction stage, a lowcomplexity noise-robust split-projection least squares (SPLS) is proposed.

Compressive sensing is a method for recording a k-sparse signal x ∈ R with (possibly noisy) linear measurements of the form y = Ax, where A ∈ Rm×n describes the measurement process. Seminal results in compressive sensing show that it is possible to recover the signal x from m = O(k log n k ) measurements and that this is tight. The model-based compressive sensing framework overcomes this lower ...