Design requirements for an 8000 frame/s dual-wavelength ratiometric chemical species tomography system, intended for hydrocarbon vapor imaging in one cylinder of a standard automobile engine, are examined. The design process is guided by spectroscopic measurements on iso-octane and by comprehensive results from laboratory phantoms and research engines, including results on temporal resolution performance. Novel image reconstruction techniques, necessary for this application, are presented. Recent progress toward implementation, including details of the optical access arrangement employed and signal-to-noise issues, is described. We present first cross-cylinder IR absorption measurements from a reduced channel-count (nontomographic) system and discuss the prospects for imaging.

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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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We present a convolutional neural network implementation for pixel processor array (PPA) sensors. PPA hardware consists of a fine-grained array of general-purpose processing elements, each capable of light capture, data storage, program execution, and communication with neighboring elements. This allows images to be stored and manipulated directly at the point of light capture, rather than having to transfer images to external processing hardware. Our CNN approach divides this array up into 4x4 blocks of processing elements, essentially trading-off image resolution for increased local memory capacity per 4x4 "pixel". We implement parallel operations for image addition, subtraction and bit-shifting images in this 4x4 block format. Using these components we formulate how to perform ternary weight convolutions upon these images, compactly store results of such convolutions, perform max-pooling, and transfer the resulting sub-sampled data to an attached micro-controller. We train ternary weight filter CNNs for digit recognition and a simple tracking task, and demonstrate inference of these networks upon the SCAMP5 PPA system. This work represents a first step towards embedding neural network processing capability directly onto the focal plane of a sensor.

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