Computational Photography: Advanced Topics. Paul Debevec

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Computational Photography: Advanced Topics Paul Debevec

Class: Computational Photography, Advanced Topics Module 1: 105 minutes Debevec,, Raskar and Tumblin 1:45: A.1 Introduction and Overview (Raskar, 15 minutes) 2:00: A.2 Concepts in Computational Photography (Tumblin,, 15 minutes) 2:15: A.3 Optics: Computable Extensions (Raskar, 30 minutes) 2:45: A.4 Sensor Innovations (Tumblin,, 30 minutes) 3:15: Q & A (15 minutes) 3:30: Break: 15 minutes Module 2: 105 minutes 3:45: B.1 Illumination As Computing (Debevec,, 25 minutes) 4:10: B.2 Scene and Performance Capture (Debevec,, 20 minutes) 4:30: B.3 Image Aggregation & Sensible Extensions (Tumblin,, 20 minutes) 4:50: B.4 Community and Social Impact (Raskar, 20 minutes) 5:10: B.4 Panel discussion (All, 20 minutes) Class Page : http://computationalphotography.org

Computational Photography: Advanced Topics A4: Sensor Innovations (30 minutes) Jack Tumblin Northwestern University

Film-Like Sensor: Array of Light Meters Film-like like Goals: Instantaneous measurement Infinite resolution; arc-min, λ Infinite sensitivity, Dyn.. Range Zero noise visible

Film-Like Photo: Photon Arrival Record Snapshot: flattened volume of space time More volume more photons less noise Movie: Repeated snapshots Ordinary Snapshot Snapshot with Motion-Blur Motion Picture (missing time!) y t y t y t x x x

6 Megapixel 3µm 3 m Always Best? http://www.6mpixel.org/en/ Independent Lab & Photo Enthusiasts site: The more pixels, the worse the image!

Noise In Camera Systems accurate, beautiful analogy:

Sensor Noise Sources Quantum Noise: Photon Rain (signal dependent) Thermal-dependent noise in semiconductors: Schott ( shot ) noise (electron-hole pairs) Imperfect materials; insulator flaws (temp, voltage, current dependent) Thermal-dependent noise in electronics: insulator leakage, phonon effects (temp dependent) RFI/EMI noise in electronics: crosstalk Good tutorial: http://www.ph.tn.tudelft.nl/courses/fip/noframes/fip-photon.html (signal dependent)

Sensor Noise Sources Quantum Noise: Photon Rain (signal dependent) Thermal-dependent noise in semiconductors: Schott ( shot ) noise (electron-hole pairs) Imperfect materials; insulator flaws (temp, voltage, current dependent) Thermal-dependent noise in electronics: insulator leakage, phonon effects Additive (fixed-strength) vs. Signal Dependent (temp dependent) RFI/EMI noise in electronics: crosstalk (signal dependent)

Fill Factor (Sensing Area / total Area)%age Interconnects, readout transistors As low as 20-30% Micro-Lenses help Aptnia (Micron Technologies)

Light-Gathering Microlenses Counteracts low fill-factor factor Improved light gathering Less Aliasing Micron Technologies, Inc Suitable for color filters

Color Sensing 3-chip: vs. 1-chip: 1 quality vs. cost http://www.cooldictionary.com/words/bayer tionary.com/words/bayer-filter.wikipedia

1-Chip Color Sensing: Bayer Grid, De-Mosaicing Estimate RGB at G cels from neighboring values http://www.cooldictionary.com/ words/bayer-filter.wikipedia

Microlenses + Color Filters Improved light gathering Fixed Alignment Less Aliasing Micron Technologies, Inc

Backside Illumination Advantages: Better fill-factor factor larger pixel sensors Less-cramped circuitry (more of it?) Seamless Surface less glare, aliasing Difficulties: Fragile: tough to create, mount, connect Opacity, Noise, sub-surface surface scatter

Back-Illuminated CCD Started ~2000 (micron tech), Now High-Performance Fairchild 4k x 4k CCD486: Thinned to 18microns + anti-reflective coating 100% fill factor, 15um pixels, 61.4 x 61.4mm sensor area Back OR Front illumination

Practical Back-Illuminated CMOS Difficult Thinning --bulk substrate removal Promising preliminary results: 1.75µm m pixels now 0.9 µm m expected (+6dB) sensitivity (~2x) (-2db) noise Sony Corp. Prototype

Color Estimation: RGBW Method 2007: Kodak Panchromatic Pixels Outperforms Bayer Grid 2X-4X sensitivity (W: no filter loss) May improve dynamic range (W >> RGB sensitivity) Colorimetry: : Direct luminance, not computed Drawbacks? de-mosaicing more difficult; earlier 4-color 4 systems (JVC: CMYW, Canon: CMGY) earned shrugs

Assorted Pixels (Nayar( et al.) Color mosaic:

Assorted Pixels (Nayar( et al.) Intensity mosaic:

Assorted Pixels (Nayar( et al.) Intensity-and and-color mosaic:

Assorted Pixels (Nayar( et al.) Intensity-and and-color-and-polarization mosaic: Other dimensions: IR? UV? Temporal? (frameless rendering) Viewpoint? (camera arrays, epipolar imaging)

Assorted Pixels (Nayar( et al.) Sony Prototype

Demosaicking ing Difficulties Under-sampling, esp. in red, blue Loss of detail, aliasing, zippering: Many good methods, no perfect answer Demosaicing by Smoothing along 1D Features, Ajdin et al., CVPR 2008 http://scien.stanford.edu/class/psych221/projects/07/dargahi&deshpande.pdf

FOVEON Sensor Multi-layer layer sensor, no color filter mosaic Senses wavelength by absorption depth http://www.foveon.com/files/cic13_hubel_final.pdf

FOVEON Sensor No under-sampling for any color, No de-mosaicking http://www.foveon.com/files/cic13_hubel_final.pdf

Hyper-Acuity Hints & SuperResolution Human Eye: Foveal receptors: 2.5 µm, ~28 ~ arc-sec sec (Curcio et al, 1990) Hyper-Acuity can detect ~1arc-sec displacement Ocular tremor contributes Superresolution: Multiple photos subpixel shifts: Assemble dense sample grid: Photoreceptors in Fovea

Penrose Pixels for SuperResolution ICCV 2007, Ben-Ezra et al., Penrose Pixels: Super-Resolution in the Detector Layout Domain Periodic: sub-pixel shifts Non-Periodic: any shift ok 8X super-res; same Back-Projection Reconstruction Method; 5.78 RMS error 2.78 RMS error

How can we choose What Matters? Image== flattened spatio-temporal temporal volume Choose Integration limits to fit the task More volume less less noise? Not always Ordinary Snapshot Time-varying snapshot Motion-tracking tracking snapshot y t y t y t x x x

Take it all: Very Long Exposure 18 Months 26 Months Postdamer Platz,, Berlin Note sun track breaks, ghost buildings 26 Month long exposure: Notice the sun tracks Michael Wesely: Open Shutter Exhibition, MOMA Museum of Modern Art, NY 2005 http://www.wesely.org/wesely/index.php

Time-Lapse without Ghosts, Jumps Computational Time-Lapse Video (SIGGRAPH 2007) Eric P. Bennett, Leonard McMillan (University of North Carolina at Chapel Hill)

Perfect Timing: Casio EXLIM Pro EX F-1F Sports: the right instant to click the shutter? y t Time bracketing: burst buffer: 6Mpix x 60 frames up to 60 Hz Data-rate limited: at 336 96 res up to 1,200 Hz x

Flash + Light-Source Blur Lighting Integration Tricks: Draw light paths in darkness Flash captures one instant 1949 AP: Pablo Picasso, Time Magazine Top 100 Artists See also: http://www.vpphotogallery.com/photog_mili_picasso.htm Lighting Doodle Projects http://tochka.jp/pikapika/ 2006/06/report_pikapika_in_kitijoji.html

Factored Time-Lapse Video Factor Whole-Day Video Seq. into: src Sky-only lighting, and Users may edit Lighting, Shadows, Reflectance, NPR SIGGRAPH 2007 Factored Time Lapse Video Sunkavalli et al.

Factored Time-Lapse Video Factor Whole-Day Video Seq. into: src Sky-only lighting, and Whole-Day, Sun-only lighting Users may edit Lighting, Shadows, Reflectance, NPR SIGGRAPH 2007 Factored Time Lapse Video Sunkavalli et al.

Factored Time-Lapse Video Factor Whole-Day Video Seq. into: src Sky-only lighting, and Whole-Day, Sun-only lighting Shadow Amount vs time Users may edit Lighting, Shadows, Reflectance, NPR SIGGRAPH 2007 Factored Time Lapse Video Sunkavalli et al.

Factored Time-Lapse Video Factor Whole-Day Video Seq. into: src Sky-only lighting, and Whole-Day, Sun-only lighting Shadow Amount vs time Edit Scene Lighting Users may edit Lighting, Shadows, Reflectance, NPR SIGGRAPH 2007 Factored Time Lapse Video Sunkavalli et al.

Factored Time-Lapse Video Factor Whole-Day Video Seq. into: src Sky-only lighting, and Whole-Day, Sun-only lighting Shadow Amount vs time Edit Scene Lighting NPR efx and more Users may edit Lighting, Shadows, Reflectance, NPR SIGGRAPH 2007 Factored Time Lapse Video Sunkavalli et al.

Spectral Range: Silicon >> Eye Aptnia (Micron Technologies)

Thermographic Cameras Two classes: Near-IR and Bolometer

Thermal IR Camera Uncooled Bolometer Arrays: Temperature-Dependent Conductance 320 x 240pixels typical Slow Temporal Response Often Shutter-free

Millimeter Wave Imaging (Radiometry) Sensitive to Temperature AND material s s reflectance High reflectance from water, metals, etc. See thru clouds and weather at some wavelengths High sensitivity, phase-sensitive sensitive (optical? RF? (1/r, not 1/r 2 ))

1-2mm Imaging Radiometry: Security Millivision Systems, Inc; At 1-2mm 1 humans glow very faintly (10-14 joule) Metals, conductors, occlude; but clothes don t Passive-only imaging: 40-60 ft camera range Weapons: Strong Silhouettes

ZCam (3Dvsystems), Shuttered Light Pulse Resolution : 1cm for 2-72 7 meters

Fife (2008) Multi-Aperture Imager 16x16 pixel overlapped sub-images Disjoint apertures, uniform spacing Many correspondences 3D depth

A Bit of Metrology History How do I weigh many small parts accurately? random error ε,, zero mean Tedious: Measure N items, one-at at-a-time: time: σ Extra-Tedious: Measure N items, M times. σ M Tolerable: Measure N SETS of (~N/2)( items. 2σ N

OLD: OLD: Hadamard Hadamard Transform Imaging Transform Imaging = D C B A L L L L 3 2 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 = 2 2 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 p p p p D C B A N sensors, N pixels, but N sensors, N pixels, but Sensors get Sensors get unique SUMS of pixels unique SUMS of pixels Each pixel is part of Each pixel is part of ~N/2 measurements ~N/2 measurements Compute pixels Compute pixels using using inverse matrix; matrix;

Compressive Sensing: Single Pixel Cam Sense large sums of pixels, not N pixels Key notion: number of pixel sums << N Support: several ground-breaking proofs

Bio-Inspired Single-Photon Detectors Mohseni,Memis: : Bio-Inspired sensor Large photon-absorption region (rhodopsin( rhodopsin) http://www.eecs.northwestern.edu/hmohseni Nano-scale hole detection (1-electron injector) Extremely small, low noise, HDR, no cooling req d http://spie.org/x19173.xml

Single-Photon Detectors Quantum Wells / Quantum Dots traps 1 electron/hole pair, from 1 absorbed photon No noisy avalanche effect Applications: Medical imaging Ghost Imaging? Secure Quantum communications?

Single-Photon Ghost Imaging Create two entangled photons: one to keep, one for scanning Kept photon tells direction, scanned photon: reflectance Covert Sensing: Interceptor can t t identify entangled photon Shih, Y., Univ Maryland: Physical Review A (DOI: 10.1103/PhysRevA.77.041801)

Flexible-Array Sensor John Rogers et al. (Beckman Institute, U of Illinois) (EECS, Northwestern Univ.)

Sensor Fabrics? Camera-Scale projects in that direction: "Scene Collages and Flexible Camera Arrays," Y. Nomura, L. Zhang and S.K. Nayar, EGSR 2007.

Other Free-Form Form Choices? Andrew Davidhazy,, RIT: http://www.rit.edu/~andpph/

Digital Sensor: Array of Light Meters What is ABSOLUTELY MANDATORY here? One sample-time? Spatial, Temporal Uniformity? Why not many? [Flutter Shutter, 2005 Raskar])? Perfect Sync, Non-adaptive, all at once? rolling shutter? Adaptive Frameless Render[2005 Dayal]?... No Spatial Overlap? Why not sinusoids? Wavelets? Gabor functions?

Common Thread: Existing Film-like like Camera quality is VERY HIGH, despite low cost. Existing sensors and cameras are just now escaping film-like like assumptions,?what can we compute with them?