Abstract—A limitation of many compressive imaging archi-tectures lies in the sequential nature of the sensing process, which leads to long sensing times. In this paper we present a novel architecture that uses fewer detectors than the number of reconstructed pixels and is able to acquire the image in a single acquisition. This paves the way for the development of video architectures that acquire several frames per second. We specifically address the diffraction problem, showing that deconvolution normally used to recover diffraction blur can be replaced by convolution of the sensing matrix, and how measurements of a 0/1 physical sensing matrix can be converted to-1/1 compressive sensing matrix without any extra acquisitions. Simulations of ...
The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if the...
This dissertation works on advanced imaging systems using multiplexed sensing and compressive sensin...
Abstract—Compressive sensing enables the reconstruction of high-resolution signals from under-sample...
Since 2004, compressive sensing (CS) has attracted considerable attentions due to its virtue of bein...
In this paper we propose a method to acquire compressed measurements for efficient video reconstruct...
Computational imaging becomes a cutting edge research area by incorporating signal/image processing ...
Compressed sensing is a new sampling theory which allows reconstructing signals using sub-Nyquist me...
Abstract. Compressive sensing (CS) is an innovative technology, allowing us to capture signals with ...
We propose a synchronous implementation of compressive imaging. This method is mathematically equiva...
This paper presents the design of a system, which can improve the reconstruction of Compressive Sens...
This paper describes a single-shot spectral imaging approach based on the concept of compressive sen...
Alternative imaging devices propose to acquire and compress images simultaneously. These devices are...
Abstract. Spatial multiplexing cameras (SMCs) acquire a (typically static) scene through a series of...
<p>Spatial multiplexing cameras (SMCs) acquire a (typically static) scene through a series of coded ...
Compressive sensing [1, 2] is a novel technique that has been gaining increasing importance in the l...
The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if the...
This dissertation works on advanced imaging systems using multiplexed sensing and compressive sensin...
Abstract—Compressive sensing enables the reconstruction of high-resolution signals from under-sample...
Since 2004, compressive sensing (CS) has attracted considerable attentions due to its virtue of bein...
In this paper we propose a method to acquire compressed measurements for efficient video reconstruct...
Computational imaging becomes a cutting edge research area by incorporating signal/image processing ...
Compressed sensing is a new sampling theory which allows reconstructing signals using sub-Nyquist me...
Abstract. Compressive sensing (CS) is an innovative technology, allowing us to capture signals with ...
We propose a synchronous implementation of compressive imaging. This method is mathematically equiva...
This paper presents the design of a system, which can improve the reconstruction of Compressive Sens...
This paper describes a single-shot spectral imaging approach based on the concept of compressive sen...
Alternative imaging devices propose to acquire and compress images simultaneously. These devices are...
Abstract. Spatial multiplexing cameras (SMCs) acquire a (typically static) scene through a series of...
<p>Spatial multiplexing cameras (SMCs) acquire a (typically static) scene through a series of coded ...
Compressive sensing [1, 2] is a novel technique that has been gaining increasing importance in the l...
The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if the...
This dissertation works on advanced imaging systems using multiplexed sensing and compressive sensin...
Abstract—Compressive sensing enables the reconstruction of high-resolution signals from under-sample...