A 132-dB Dynamic-Range Global-Shutter Stacked Architecture for High-Performance Imagers

Xhakoni, Adi; Gielen, Georges

doi:10.1109/tcsii.2014.2319972

Cited by 8 publications

(5 citation statements)

References 11 publications

(11 reference statements)

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“…Exotic Sensors: A plethora of unconventional sensors has been explored in previous work. For example, HDR imaging has been demonstrated with multiple exposures built into the pixels [11], [12], using events to signal overflow of the pixel buffer and reset it [32], using precomputation on the focal plane with spatially varying and adaptive exposures [33], [34], using multiple photodiodes with various gains in each pixel [35], or using logarithmic compression at the photosensor [36], as well as many other approaches [13], [37], [38], [39]. Per-pixel coded exposures have also been realized in several dedicated sensors [40], [41], [42], [43], for example with applications to video compressive sensing.…”

Section: Hdr Imaging and Video Compressive Sensingmentioning

confidence: 99%

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Neural Sensors: Learning Pixel Exposures for HDR Imaging and Video Compressive Sensing With Programmable Sensors

Martel

Müller

Carey

et al. 2020

IEEE Trans. Pattern Anal. Mach. Intell.

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Camera sensors rely on global or rolling shutter functions to expose an image. This fixed function approach severely limits the sensors' ability to capture high-dynamic-range (HDR) scenes and resolve high-speed dynamics. Spatially varying pixel exposures have been introduced as a powerful computational photography approach to optically encode irradiance on a sensor and computationally recover additional information of a scene, but existing approaches rely on heuristic coding schemes and bulky spatial light modulators to optically implement these exposure functions. Here, we introduce neural sensors as a methodology to optimize per-pixel shutter functions jointly with a differentiable image processing method, such as a neural network, in an end-to-end fashion. Moreover, we demonstrate how to leverage emerging programmable and re-configurable sensor-processors to implement the optimized exposure functions directly on the sensor. Our system takes specific limitations of the sensor into account to optimize physically feasible optical codes and we demonstrate state-of-the-art performance for HDR and high-speed compressive imaging in simulation and with experimental results. Index Terms-high-dynamic range imaging, video compressive sensing, high-speed imaging, programmable sensors, vision chip, deep neural networks, end-to-end optimization !

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Section: Hdr Imaging and Video Compressive Sensingmentioning

confidence: 99%

“…Here, we show a single coded exposure captured with our prototype camera and several frames of the high-speed video reconstructed from this image showing an exploding balloon. [12], [13] or high-speed imaging [14], [15]. However, these devices cannot be easily reconfigured for other applications.…”

Section: Introductionmentioning

confidence: 99%

Neural Sensors: Learning Pixel Exposures for HDR Imaging and Video Compressive Sensing With Programmable Sensors

Martel

Müller

Carey

et al. 2020

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Signal storage in the charge domain has been developed 4 and has the important advantage of eliminating additional kTC noise from capacitor charging, at the expense of customized PPD design. Storage in the voltage domain across capacitors is somewhat easier to implement and many examples exist [5][6][7][8][9] , but storage capacitors may occupy a large part of the pixel and restrict the minimum pixel size and the fill factor.…”

Section: Introductionmentioning

confidence: 99%

“…Two-tier CIS with split detection and storage layers 6 could be valuable in solving many of these challenges and offer improved performance in cases where the increased complexity can be justified. This paper describes the design and the operation of a new 10T image sensor, the CIS109, featuring global reset, global shutter and per-pixel CDS.…”

Section: Introductionmentioning

confidence: 99%

A global shutter CMOS image sensor for hyperspectral imaging

et al. 2015

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“…Although this technique performs well for still images, it creates artefacts if motion occurs during multi-exposure. Specialised sensing architectures capable of extending the dynamic range through single exposure [2][3][4] are thus highly demanded at present [5]. This is also the case for vision sensors that, unlike imagers, are intended not to simply provide high-quality images but to support the automatic extraction of meaningful information from the activity, i.e.…”

Section: Introductionmentioning

confidence: 99%

High dynamic range adaptation for ROI tracking based on reconfigurable concurrent dual‐sensing

et al. 2014

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A single-exposure technique to extend the dynamic range of vision sensors is presented. It is particularly suitable for vision algorithms requiring region-of-interest (ROI) tracking under varying illumination conditions. The operation is supported by two intertwined photodiodes at pixel level and two digital registers at the periphery of the pixel matrix. These registers divide the focal plane into independent regions within which automatic concurrent adjustment of the integration time takes place for each frame. At pixel level, one of the photodiodes senses the pixel value itself, whereas the other, in collaboration with its counterparts in every prescribed ROI, senses the mean illumination of that specific ROI. An additional circuitry interconnecting both photodiodes asynchronously determines the integration period for each ROI according to its mean illumination. The experimental results for a quarter video graphics array prototype CMOS vision sensor are reported.Introduction: The most usual technique for imagers to deal with scenes featuring high dynamic range (HDR) consists in taking multiple captures per frame with different exposure periods and subsequently combining them [1]. Although this technique performs well for still images, it creates artefacts if motion occurs during multi-exposure. Specialised sensing architectures capable of extending the dynamic range through single exposure [2-4] are thus highly demanded at present [5]. This is also the case for vision sensors that, unlike imagers, are intended not to simply provide high-quality images but to support the automatic extraction of meaningful information from the activity, i.e. motion, taking place in a scene. Despite this fundamental difference between the targeted functionality of imagers and vision sensors, the latter commonly makes use of the HDR techniques devised for the former. The development of specific HDR techniques tailored for the requirements of vision algorithms has received little attention. In this Letter, we describe a sensing architecture particularly suitable for one of the basic tasks implemented by vision algorithms: region-of-interest (ROI) tracking. Once a certain ROI is spotted, a vision algorithm typically tracks it across the scene while carrying out the prescribed analytics. This tracking and the corresponding analytics must not be affected by variations in the illumination over the ROI. Indeed, the priority should be to adapt the capture for that ROI while ensuring that new ROIs can still be detected and adapted in case they enter the scene. This is exactly the functionality provided by the proposed architecture.

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A 132-dB Dynamic-Range Global-Shutter Stacked Architecture for High-Performance Imagers

Cited by 8 publications

References 11 publications

Neural Sensors: Learning Pixel Exposures for HDR Imaging and Video Compressive Sensing With Programmable Sensors

Neural Sensors: Learning Pixel Exposures for HDR Imaging and Video Compressive Sensing With Programmable Sensors

A global shutter CMOS image sensor for hyperspectral imaging

High dynamic range adaptation for ROI tracking based on reconfigurable concurrent dual‐sensing

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