Edge Compression: An Integrated Framework for Compressive Imaging Processing on CAVs

Lu, Sidi; Yuan, Xin; Shi, Wei

doi:10.1109/sec50012.2020.00017

Cited by 29 publications

(19 citation statements)

References 49 publications

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“…Our proposed network is not limited to the spectral compressive imaging such as CASSI and similar systems [46,20], it can also be used in the video snapshot compressive imaging systems [28,30,29,45]. Our work is paving the way of real applications of snapshot compressive imaging [19,44].…”

Section: Discussionmentioning

confidence: 99%

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

Huang

Dong

Yuan

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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In coded aperture snapshot spectral imaging (CASSI) system, the real-world hyperspectral image (HSI) can be reconstructed from the captured compressive image in a snapshot. Model-based HSI reconstruction methods employed hand-crafted priors to solve the reconstruction problem, but most of which achieved limited success due to the poor representation capability of these hand-crafted priors. Deep learning based methods learning the mappings between the compressive images and the HSIs directly achieved much better results. Yet, it is nontrivial to design a powerful deep network heuristically for achieving satisfied results. In this paper, we propose a novel HSI reconstruction method based on the Maximum a Posterior (MAP) estimation framework using learned Gaussian Scale Mixture (GSM) prior. Different from existing GSM models using hand-crafted scale priors (e.g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN). Furthermore, we also propose to estimate the local means of the GSM models by the DCNN. All the parameters of the MAP estimation algorithm and the DCNN parameters are jointly optimized through end-to-end training. Extensive experimental results on both synthetic and real datasets demonstrate that the proposed method outperforms existing state-of-the-art methods. The code is available at https://see.xidian.edu.cn/faculty/ wsdong/Projects/DGSM-SCI.htm.

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Section: Discussionmentioning

confidence: 99%

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

Huang

Dong

Yuan

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Also, a new research direction has appeared with deploying a video compressive sensing system with edge computing to optimize the memory storage and bandwidth [89]. In addition, theoretical studies on detection algorithms directly from the snapshot compressed measurement have already started [90]. Finally, we can say that compressive sensing allows us to think about data, complexity, algorithms and hardware at the same time.…”

Section: Compressive Sensing: Research Challenges and Opportunitiesmentioning

confidence: 99%

An Overview on Deep Learning Techniques for Video Compressive Sensing

et al. 2022

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The use of compressive sensing in several applications has allowed to capture impressive results, especially in various applications such as image and video processing and it has become a promising direction of scientific research. It provides extensive application value in optimizing video surveillance networks. In this paper, we introduce recent state-of-the-art video compressive sensing methods based on neural networks and categorize them into different categories. We compare these approaches by analyzing the networks architectures. Then, we present their pros and cons. The general conclusion of the paper identify open research challenges and point out future research directions. The goal of this paper is to overview the current approaches in image and video compressive sensing and demonstrate their powerful impact in computer vision when using well designed compressive sensing algorithms.

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“…Specifically, computational imaging optimizes the design of the imaging system by considering the afterwards processing. Towards this end, one can build systems optimized for specific high-level vision tasks, and conduct analysis directly from the coded measurements [227]. Such method can capture information tailored for the desired task, and thus is of improved performance and largely reduced bandwidth and memory requirements.…”

Section: Ai Guides High-level Task Of CI Systemmentioning

confidence: 99%

“…We expect to deploy our proposed framework to intelligent mobile platforms in various fields in the near future. Selfdriving car is the intelligent transportation vehicle that will most likely be put into use [227]; meanwhile, unmanned aerial vehicle (UAV), satellite and unmanned underwater vehicle will be applied in military or civilian scenes. Different mobile platforms have varying loads and requirements for imaging dynamic range, resolution and frame rate, to perform different tasks such as detection, tracking and navigation.…”

Section: Extensions / Generalization To Related Systemsmentioning

confidence: 99%

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Suo¹,

Zhang²,

Gong³

et al. 2021

Preprint

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Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision. Recent explosive progresses in Artificial Intelligence (AI) have shown great potential to develop advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterwards" mechanism cannot meet this unprecedented demand. Differently, Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real systems by integrating deep learning algorithms into the mobile vision platform to achieve the closed loop of intelligent acquisition, processing and decision making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advances of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Motivated by the fact that most existing studies only loosely connect CI and AI (usually using AI to improve the performance of CI and only limited works have deeply connected them), in this work, we propose a framework to deeply integrate CI and AI by using the example of self-driving vehicles with high-speed communication, edge computing and traffic planning. Finally, we outlook the future of CI plus AI by investigating new materials, brain science and new computing techniques to shed light on new directions of mobile vision systems.

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Edge Compression: An Integrated Framework for Compressive Imaging Processing on CAVs

Cited by 29 publications

References 49 publications

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

An Overview on Deep Learning Techniques for Video Compressive Sensing

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

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