A 1/2.5 inch VGA 400 fps CMOS Image Sensor With High Sensitivity for Machine Vision

Xu, Ruoyu; Ng, Wai Chiu; Yuan, Jie; Yin, Shouyi; Wei, Shaojun

doi:10.1109/jssc.2014.2345018

Cited by 35 publications

(9 citation statements)

References 35 publications

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“…High-speed image capture is one of the fundamental tasks for numerous industrial and scientific applications such as target tracking, optical scientific measurement, autonomous vehicles [1][2][3][4][5], etc. Generally, in high-speed vision systems, the challenges of insufficient bandwidth and storage are increasingly severe and gradually become the bottlenecks.…”

Section: Introductionmentioning

confidence: 99%

A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression

et al. 2017

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This paper presents a compact vector quantizer based on the self-organizing map (SOM), which can fulfill the data compression task for high-speed image sequence. In this vector quantizer, we solve the most severe computational demands in the codebook learning mode and the image encoding mode by a reconfigurable complete-binary-adder-tree (RCBAT), where the arithmetic units are thoroughly reused. In this way, the hardware efficiency of our proposed vector quantizer is greatly improved. In addition, by distributing the codebook into the multi-parallel processing sub-blocks, our design obtains a high compression speed successfully. Furthermore, a mechanism of partial vector-component storage (PVCS) is adopted to make the compression ratio adjustable. Finally, the proposed vector quantizer has been implemented on the field programmable gate array (FPGA). The experimental results indicate that it respectively achieves a compression speed of 500 frames/s and a million connections per second (MCPS) of 28,494 (compression ratio is 64) when working at 79.8 MHz. Besides, compared with the previous scheme, our proposed quantizer achieves a reduction of 8% in hardware usage and an increase of 33% in compression speed. This means the proposed quantizer is hardware-efficient and can be used for high-speed image compression.

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Section: Introductionmentioning

confidence: 99%

A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression

et al. 2017

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“…However, the 4T-APS has reduced performance in lowlight illumination producing delaying images due to slow charge transfer from the photodiode to the floating drain capacitor. Even though this can be overcome with various techniques, it usually results in affecting the sensitivity performance [4]. On the other hand the CTIA topology has high sensitivity that depends on a feedback capacitor which can be very small.…”

Section: Conventional Cis Pixel Architecturesmentioning

confidence: 99%

Compact pixel architecture for CMOS lateral flow immunoassay readout systems

Pilavaki

Valente

Serdijn

et al. 2017

2017 13th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME)

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A novel pixel architecture for CMOS image sensors is presented. It uses only one amplifier for both integration of the photocurrent and in-pixel noise cancelation, thus minimizing power consumption. The circuit is specifically designed to be used in readout systems for lateral flow immunoassays. In addition a switching technique is introduced enabling the use of column correlated double sampling technique in capacitive transimpedance amplifier pixel architectures without the use of any memory cells. As a result the reset noise which is crucial in these architectures can be suppressed. The circuit has been designed in a 0.35-μm CMOS technology and simulations are presented to show its performance.

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“…Most conventional 4T CMOS image sensors generally operate in a rolling shutter mode, and 5T designs add the ability to perform global reset and global snapshot 2 . Practically all modern CIS designs are based on the pinned photodiode (PPD) structure 3 due to its ability to perform efficient correlated double sampling (CDS) and eliminate the reset noise present in 3T designs.…”

Section: Introductionmentioning

confidence: 99%