Quantum-inspired computational imaging

Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip

CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends

CMOS Image Sensors (CIS) are key for imaging technol-ogies. These chips are conceived for capturing opticalscenes focused on their surface, and for delivering elec-trical images, commonly in digital format. CISs may incor-porate intelligence; however, their smartness basicallyconcerns calibration, error correction and other similartasks. The term CVISs (CMOS VIsion Sensors) definesother class of sensor front-ends which are aimed at per-forming vision tasks right at the focal plane. They havebeen running under names such as computational imagesensors, vision sensors and silicon retinas, among others. CVIS and CISs are similar regarding physical imple-mentation. However, while inputs of both CIS and CVISare images captured by photo-sensors placed at thefocal-plane, CVISs primary outputs may not be imagesbut either image features or even decisions based on thespatial-temporal analysis of the scenes. We may hencestate that CVISs are more “intelligent” than CISs as theyfocus on information instead of on raw data. Actually,CVIS architectures capable of extracting and interpretingthe information contained in images, and prompting reac-tion commands thereof, have been explored for years inacademia, and industrial applications are recently ramp-ing up.One of the challenges of CVISs architects is incorporat-ing computer vision concepts into the design flow. Theendeavor is ambitious because imaging and computervision communities are rather disjoint groups talking dif-ferent languages. The Cellular Nonlinear Network Univer-sal Machine (CNNUM) paradigm, proposed by Profs.Chua and Roska, defined an adequate framework forsuch conciliation as it is particularly well suited for hard-ware-software co-design [1]-[4]. This paper overviewsCVISs chips that were conceived and prototyped at IMSEVision Lab over the past twenty years. Some of them fitthe CNNUM paradigm while others are tangential to it. Allthem employ per-pixel mixed-signal processing circuitryto achieve sensor-processing concurrency in the quest offast operation with reduced energy budget.Junta de Andalucía TIC 2012-2338Ministerio de Economía y Competitividad TEC 2015-66878-C3-1-R y TEC 2015-66878-C3-3-

Computational periscopy with an ordinary digital camera

Computing the amounts of light arriving from different directions enables a diffusely reflecting surface to play the part of a mirror in a periscope—that is, perform non-line-of-sight imaging around an obstruction. Because computational periscopy has so far depended on light-travel distances being proportional to the times of flight, it has mostly been performed with expensive, specialized ultrafast optical systems^1,2,3,4,5,6,7,8,9,10,11,12. Here we introduce a two-dimensional computational periscopy technique that requires only a single photograph captured with an ordinary digital camera. Our technique recovers the position of an opaque object and the scene behind (but not completely obscured by) the object, when both the object and scene are outside the line of sight of the camera, without requiring controlled or time-varying illumination. Such recovery is based on the visible penumbra of the opaque object having a linear dependence on the hidden scene that can be modelled through ray optics. Non-line-of-sight imaging using inexpensive, ubiquitous equipment may have considerable value in monitoring hazardous environments, navigation and detecting hidden adversaries.We thank F. Durand, W. T. Freeman, Y. Ma, J. Rapp, J. H. Shapiro, A. Torralba, F. N. C. Wong and G. W. Wornell for discussions. This work was supported by the Defense Advanced Research Projects Agency (DARPA) REVEAL Program contract number HR0011-16-C-0030. (HR0011-16-C-0030 - Defense Advanced Research Projects Agency (DARPA) REVEAL Program)Accepted manuscrip

Ghost imaging with the human eye

Computational ghost imaging relies on the decomposition of an image into patterns that are summed together with weights that measure the overlap of each pattern with the scene being imaged. These tasks rely on a computer. Here we demonstrate that the computational integration can be performed directly with the human eye. We use this human ghost imaging technique to evaluate the temporal response of the eye and establish the image persistence time to be around 20 ms followed by a further 20 ms exponential decay. These persistence times are in agreement with previous studies but can now potentially be extended to include a more precise characterisation of visual stimuli and provide a new experimental tool for the study of visual perception