Power-efficient CMOS image acquisition system based on compressive sampling

Katic, Nikola; Kamal, Mahdad Hosseini; Kilic, Mustafa Sabri; Schmid, Alexandre; Leblebici, Yusuf

doi:10.1109/mwscas.2013.6674910

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…In order to avoid complex optical systems, some on-chip compressed sensing schemes have been proposed in conventional imaging sensors, which could realize efficient data reading and reduced data bandwidth. Conventional CMOS image sensor converts light intensity into electrical signals for each pixel individually, while CS CMOS image sensors only sample a small set of random pixel summations [15][16][17][18][19][20][21], which can reduce the size of output data, analog to digital conversion (ADC) operations and the sensor power consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Since 2006, the theory of compressed sensing (CS) [12][13][14] has been proposed for efficient data sampling and high-fidelity sparse recovery. Implementing the idea of CS on the CMOS (complementary metal-oxide-semiconductor) sensor chip has been recently proposed [15][16][17][18][19][20][21] and shows promising data throughput reduction on conventional sensing techniques.…”

Section: Introductionmentioning

confidence: 99%

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On-Chip Compressive Sensing with a Single-Photon Avalanche Diode Array

Qiu

Wang

Kong

et al. 2023

Sensors

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Single-photon avalanche diodes (SPADs) are novel image sensors that record photons at extremely high sensitivity. To reduce both the required sensor area for readout circuits and the data throughput for SPAD array, in this paper, we propose a snapshot compressive sensing single-photon avalanche diode (CS-SPAD) sensor which can realize on-chip snapshot-type spatial compressive imaging in a compact form. Taking advantage of the digital counting nature of SPAD sensing, we propose to design the circuit connection between the sensing unit and the readout electronics for compressive sensing. To process the compressively sensed data, we propose a convolution neural-network-based algorithm dubbed CSSPAD-Net which could realize both high-fidelity scene reconstruction and classification. To demonstrate our method, we design and fabricate a CS-SPAD sensor chip, build a prototype imaging system, and demonstrate the proposed on-chip snapshot compressive sensing method on the MINIST dataset and real handwritten digital images, with both qualitative and quantitative results.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On-Chip Compressive Sensing with a Single-Photon Avalanche Diode Array

Qiu

Wang

Kong

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…The aforementioned random pixel summation can be expressed by matrix operation ⋅ , where ϕ is called CS measurement matrix. At present, existing CS image sensor designs use random measurement matrices to guide how pixel outputs are summed [5,11,12,14,18]. The randomness is for satisfying the incoherence requirement or restricted isometry property (RIP) suggested by CS theories [3,6].…”

Section: Techniquesmentioning

confidence: 99%

“…The drawbacks of this circuit include complicated pixel cell design and signal swing challenges due to summing a large number of pixels. The design in [12] also uses a dual bit-line structure to accommodate weighting values of 1 and -1. Its pixel outputs are in the form of charge and hence pixel summations are carried out by charge amplifiers.…”

Section: B Previously Proposed Compressive Sensing Image Sensorsmentioning

confidence: 99%

Design of Scalable Hardware-Efficient Compressive Sensing Image Sensors

2018

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“…In principle, the generation of randomness timing clocks and large‐scale matrix–vector multiplication (MVM) in CS systems consists of complementary metal–oxide–semiconductor circuits and modules. [ 9 ] While previous research has focused on developing emerging high‐performance sensors, complex sampling control modules, such as OTS generation and high‐intensity MVM operations, continue to be cumbersome, limiting scalability and sampling speed. [ 10–13 ] Consequently, a hardware implementation that integrates compression and encryption still faces significant obstacles.…”

Section: Introductionmentioning

confidence: 99%

All‐in‐One Compression and Encryption Engine Based on Flexible Polyimide Memristor

Wang

Xin

et al. 2023

Small Science

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It is anticipated that the rapid development of the Internet of Things (IoT) will improve the quality of human life. Nonetheless, large amounts of data need to be replicated, stored, processed, and shared, posing formidable challenges to communication bandwidth and information security. Herein, it is reported that polyimide (PI) threshold‐switching memristors exhibit Gaussian conductance and randomly set voltage distribution with nonideal properties to create a compression and encryption engine with a single chip. The Gaussian conductance distribution is used to achieve compressed sensing (CS) to integrate encryption into compression, and the spontaneous formation of the one‐time‐sampling measurement matrix satisfies absolute security. Moreover, the bitstreams generated by randomly distributed set voltages are used to diffuse the ciphertext from CS to improve security. The engine is shown to be secure even if the eavesdropper knows both the plaintext and the corresponding ciphertext. It has compression performance advantages that take both efficiency and security into account. In addition, due to the superior high temperature and mechanical properties of PI, the engine can continue to function normally in harsh environments. Herein, an excellent solution is offered for ensuring the efficiency and security of IoT.

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Power-efficient CMOS image acquisition system based on compressive sampling

Cited by 8 publications

References 8 publications

On-Chip Compressive Sensing with a Single-Photon Avalanche Diode Array

On-Chip Compressive Sensing with a Single-Photon Avalanche Diode Array

Design of Scalable Hardware-Efficient Compressive Sensing Image Sensors

All‐in‐One Compression and Encryption Engine Based on Flexible Polyimide Memristor

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