Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction

Trevisi, Marco; Akbari, Ali; Trocan, Maria; Rodríguez-Vázquez, Á.; Carmona-Galán, Ricardo

doi:10.1109/tcsvt.2019.2892178

Cited by 16 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the above, we can see that the bad effects resulting from the extraction of contexts can be suppressed by the block (Trevisi et al, 2020), and Multi-Hypothesis Reweighted TIKhonov (MH-RTIK) (Chen C. et al, 2020) merging, therefore, the quality improvement from contextsbased allocation is further enhanced.…”

Section: Effects Of Block Sizesmentioning

confidence: 98%

“…Reconstruction is deployed at the decoder, and it uses quantized measurements to reconstruct the video sequence by the CS recovery algorithm. At present, the reconstruction can be implemented by one of the three types: frame-by-frame (Chen Y. et al, 2020 ; Trevisi et al, 2020 ), three-dimensional (3D) (Qiu et al, 2015 ; Tachella et al, 2020 ), and distributed strategies (Zhang et al, 2020 ; Zhen et al, 2020 ). The frame-by-frame reconstruction performs a CS recovery algorithm to reconstruct each video frame independently, and it has a poor rate-distortion performance due to neglecting the correlations between frames.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things

Yang

Sun

2022

Front. Plant Sci.

View full text Add to dashboard Cite

Internet of Things (IoT) realizes the real-time video monitoring of plant propagation or growth in the wild. However, the monitoring time is seriously limited by the battery capacity of the visual sensor, which poses a challenge to the long-working plant monitoring. Video coding is the most consuming component in a visual sensor, it is important to design an energy-efficient video codec in order to extend the time of monitoring plants. This article presents an energy-efficient Compressive Video Sensing (CVS) system to make the visual sensor green. We fuse a context-based allocation into CVS to improve the reconstruction quality with fewer computations. Especially, considering the practicality of CVS, we extract the contexts of video frames from compressive measurements but not from original pixels. Adapting to these contexts, more measurements are allocated to capture the complex structures but fewer to the simple structures. This adaptive allocation enables the low-complexity recovery algorithm to produce high-quality reconstructed video sequences. Experimental results show that by deploying the proposed context-based CVS system on the visual sensor, the rate-distortion performance is significantly improved when comparing it with some state-of-the-art methods, and the computational complexity is also reduced, resulting in a low energy consumption.

show abstract

Section: Effects Of Block Sizesmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things

Yang

Sun

2022

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Besides, in order to verify the real-time performance of the signal reconstruction process of the compressed sensing algorithm based on the algorithm proposed in this paper, in the experiment, this algorithm was compared with the current best compressed sensing reconstruction algorithm, such as OMP, StOMP, CoSaMP, GOMP, GROMP, and SPL, etc. [21][22][23][24][25].…”

Section: (3) Sparse Reconstructionmentioning

confidence: 99%

A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

et al. 2021

View full text Add to dashboard Cite

The high complexity of the reconstruction algorithm is the main bottleneck of the hyperspectral image (HSI) compression technology based on compressed sensing. Compressed sensing technology is an important tool for retrieving the maximum number of HSI scenes on the ground. However, the complexity of the compressed sensing algorithm is limited by the energy and hardware of spaceborne equipment. Aiming at the high complexity of compressed sensing reconstruction algorithm and low reconstruction accuracy, an equivalent model of the invertible transformation is theoretically derived by us in the paper, which can convert the complex invertible projection training model into the coupled dictionary training model. Besides, aiming at the invertible projection training model, the most competitive task-driven invertible projection matrix learning algorithm (TIPML) is proposed. In TIPML, we don’t need to directly train the complex invertible projection model, but indirectly train the invertible projection model through the training of the coupled dictionary. In order to improve the accuracy of reconstructed data, in the paper, the singular value transformation is proposed. It has been verified that the concentration of the dictionary is increased and that the expressive ability of the dictionary has not been reduced by the transformation. Besides, two-loop iterative training is established to improve the accuracy of data reconstruction. Experiments show that, compared with the traditional compressed sensing algorithm, the compressed sensing algorithm based on TIPML has higher reconstruction accuracy, and the reconstruction time is shortened by more than a hundred times. It is foreseeable that the TIPML algorithm will have a huge application prospect in the field of HSI compression.

show abstract

“…Similarly, according to (22), θ H n θ m ðα s ðmÞ þ α ε ðmÞÞ follows a complex Gaussian distribution with the parameters that are shown in Equations ( 27) and (28).…”

Section: Posterior Probability Distribution Model Constructionmentioning

confidence: 99%

“…When the Gaussian random matrix [22] is used as the linear measurement matrix, the elements in

Φ

follow the Gaussian distribution with a mean of 0 and a variance of

1 / M

. In addition,

Ψ

is a unitary matrix.…”

Section: Posterior Probability Distribution‐based Compressive Nbi Det...mentioning

confidence: 99%

Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication

Shi

Zhang

Qian

et al. 2021

IET Signal Processing

View full text Add to dashboard Cite

The Shannon-Nyquist sampling theorem that is based on narrowband interference (NBI) detection and parameter estimation methods in direct sequence spread spectrum (DSSS) communication is limited by the high sampling rate. Compressive sensing (CS) is adopted to address the problem. But it will change the signal nature, which leads to the unavailability of Shannon-Nyquist sampling theorem-based interference detection and parameter estimation methods. According to the different posterior probability distribution features of NBI, DSSS signal, and noise in compressed domain, a posterior probability model of whether NBI exists in the received signal is constructed by using the compressed measurements. The posterior probability that whether NBI exists in the received signal is employed as the feature parameters of NBI detection and parameter estimation. With the feature parameters detected, NBI detection can be achieved and the edge location of NBI components can be located. The relationship between the edge location of NBI components and the edge frequency of NBI is constructed, which will contribute to estimate the NBI edge frequency. The numerical simulation results demonstrate that the proposed method can effectively achieve NBI detection and parameter estimation in the compressed domain and it performs significantly better than the other existing methods.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction

Cited by 16 publications

References 30 publications

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things

A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication

Contact Info

Product

Resources

About