Single‐pixel compressive imaging based on motion compensation

Wang, Zelong; Zhu, Jun

doi:10.1049/iet-ipr.2018.5741

Cited by 3 publications

(2 citation statements)

References 36 publications

(35 reference statements)

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“…A huge amount of information redundancy (spatial and temporal) is required for classic video stream-based algorithms. Indeed, sensor-level compressive sensing for video capture will be an important step towards low power video applications [36][37][38][39][40][41].…”

Section: Io Architecturementioning

confidence: 99%

Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

et al. 2019

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In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.

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Section: Io Architecturementioning

confidence: 99%

Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

et al. 2019

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“…In the case of video sequences, a small number (e.g., 5-20%) of random linear measurements are observed for each frame. These measurements are computed by processing raw pixel data of a conventional sensor or even directly coming out from a modified sensor in which the processing circuity is integrated as a part of the sensing circuity itself and realized in the analog domain [3,4]. A linear reconstruction by inverse transform, cannot, in general, recover the signal from a small number of measurements.…”

Section: Introductionmentioning

confidence: 99%

Compressive sensed video recovery via iterative thresholding with random transforms

Belyaev

Codreanu²,

Juntti³

et al. 2020

IET image process

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We consider the problem of compressive sensed video recovery via iterative thresholding algorithm. Traditionally, it is assumed that some fixed sparsifying transform is applied at each iteration of the algorithm. In order to improve the recovery performance, at each iteration the thresholding could be applied for different transforms in order to obtain several estimates for each pixel. Then the resulting pixel value is computed based on obtained estimates using simple averaging. However, calculation of the estimates leads to significant increase in reconstruction complexity. Therefore, we propose a heuristic approach, where at each iteration only one transform is randomly selected from some set of transforms. First, we present a simple example, when block-based 2-D discrete cosine transform is used as the sparsifying transform, and show that the random selection of the block size at each iteration significantly outperforms the case when fixed block size is used. Then, we show that similar improvement can be achieved, when a random shift of the blocks grid is introduced, or when random 2-D discrete wavelet transform is applied at each iteration. Second, building on these simple examples, we apply the proposed approach when video block-matching and 3D filtering (VBM3D) is used for the thresholding and show that the recovery performance could be improved as compared with the recovery based on VBM3D with fixed transform. Finally, extending the introduced approach, we propose to also randomly select a frame residual computation algorithm as well and show that it could provide additional gain as well.

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Moving Target Imaging via Computational Ghost Imaging Combined With Artificial Bee Colony Optimization

Zheng

Chen

et al. 2022

IEEE Trans. Instrum. Meas.

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Single‐pixel compressive imaging based on motion compensation

Cited by 3 publications

References 36 publications

Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

Compressive sensed video recovery via iterative thresholding with random transforms

Moving Target Imaging via Computational Ghost Imaging Combined With Artificial Bee Colony Optimization

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