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D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
Computational Photography
Computational Photography
Future Directions
Future Directions
Wavefront Coding: 10X Depth of Field
Wavefront Coding: 10X Depth of Field
Wavefront Coding: 10X Depth of Field
Wavefront Coding: 10X Depth of Field
Integral Photography
Integral Photography
Georgeiv et al 2006
Georgeiv et al 2006
Light field photography using a handheld plenoptic camera
Light field photography using a handheld plenoptic camera
Conventional versus light field camera
Conventional versus light field camera
Conventional versus light field camera
Conventional versus light field camera
Conventional versus light field camera
Conventional versus light field camera
Prototype camera
Prototype camera
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
Digital refocusing
Digital refocusing
Example of digital refocusing
Example of digital refocusing
Extending the depth of field
Extending the depth of field
Future Directions
Future Directions
Novel Sensors
Novel Sensors
Foveon: All Colors at a Single Pixel
Foveon: All Colors at a Single Pixel
High Dynamic Range
High Dynamic Range
Gradient Camera
Gradient Camera
High Dynamic Range Images
High Dynamic Range Images
Natural Scene Properties
Natural Scene Properties
Original Image Intensity values ranging from 0 to 1800 Intensity ramp
Original Image Intensity values ranging from 0 to 1800 Intensity ramp
Gradient Camera
Gradient Camera
Camera Pipeline
Camera Pipeline
Detail Preserving
Detail Preserving
Quantization
Quantization
Demodulating Cameras
Demodulating Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
Demodulating Cameras
Demodulating Cameras
3D Cameras
3D Cameras
ZCam (3Dvsystems), Shuttered Light Pulse
ZCam (3Dvsystems), Shuttered Light Pulse
Graphics can inserted behind and between characters
Graphics can inserted behind and between characters
Canesta: Modulated Emitter
Canesta: Modulated Emitter
Motion _ _
Motion _ _
Line Scan Camera: PhotoFinish 2000 Hz
Line Scan Camera: PhotoFinish 2000 Hz
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
Fluttered Shutter Camera
Fluttered Shutter Camera
Figure 2 results
Figure 2 results
Rectified Image to make motion lines parallel to scan lines
Rectified Image to make motion lines parallel to scan lines
Approximate cutout of the blurred image containing the taxi
Approximate cutout of the blurred image containing the taxi
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
D.2: Smart Optics, Modern Sensors and Future Cameras
Novel Sensors
Novel Sensors
Perspective
Perspective
Multiperspective Camera
Multiperspective Camera
Fantasy Configurations
Fantasy Configurations
Computational Photography
Computational Photography
Goals
Goals
Acknowledgements
Acknowledgements
IEEE Computer Special Issue on Computational Photography
IEEE Computer Special Issue on Computational Photography
Computational Photography Mastering New Techniques for Lenses,
Computational Photography Mastering New Techniques for Lenses,
Siggraph 2006 Computational Photography Papers
Siggraph 2006 Computational Photography Papers
Computational Photography
Computational Photography
Schedule
Schedule
Computational Photography Panel Discussion
Computational Photography Panel Discussion
Computational Photography Panel Discussion
Computational Photography Panel Discussion
Computational Photography Panel Discussion
Computational Photography Panel Discussion
Dream of A New Photography
Dream of A New Photography
Computational Photography
Computational Photography

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D.2: Smart Optics, Modern Sensors and Future Cameras

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1 D.2: Smart Optics, Modern Sensors and Future Cameras

D.2: Smart Optics, Modern Sensors and Future Cameras

Ramesh Raskar Mitsubishi Electric Research Labs

Course WebPage : http://www.merl.com/people/raskar/photo

2 Computational Photography

Computational Photography

Novel Cameras

Generalized Sensor

Generalized Optics

Processing

Light Sources

3 Future Directions

Future Directions

Scientific Imaging Tomography, Deconvolution, Coded Aperture Imaging Computational Illumination Light stages, Domes, Light waving, Towards 8D Smart Optics Handheld Light field camera, Programmable imaging/aperture Smart Sensors HDR Cameras, Gradient Sensing, Line-scan Cameras, Demodulators Speculations

4 Wavefront Coding: 10X Depth of Field

Wavefront Coding: 10X Depth of Field

Traditional Lens: Defocus (‘circle of confusion) dependent on distance from plane of focus

http://www.cdm-optics.com/site/extended_dof.php

5 Wavefront Coding: 10X Depth of Field

Wavefront Coding: 10X Depth of Field

Traditional Lens: Defocus dependent on distance from plane of focus Cubic Phase Plate Defocus nearly independent of distance All points ‘blurred’ Deconvolve to get sharper image

http://www.cdm-optics.com/site/extended_dof.php

6 Integral Photography

Integral Photography

Todor Georgeiv et al 2006

7 Georgeiv et al 2006

Georgeiv et al 2006

8 Light field photography using a handheld plenoptic camera

Light field photography using a handheld plenoptic camera

Ren Ng, Marc Levoy, Mathieu Br?dif, Gene Duval, Mark Horowitz and Pat Hanrahan

9 Conventional versus light field camera

Conventional versus light field camera

10 Conventional versus light field camera

Conventional versus light field camera

11 Conventional versus light field camera

Conventional versus light field camera

uv-plane

st-plane

12 Prototype camera

Prototype camera

4000 ? 4000 pixels ? 292 ? 292 lenses = 14 ? 14 pixels per lens

Contax medium format camera

Kodak 16-megapixel sensor

13 D.2: Smart Optics, Modern Sensors and Future Cameras
14 Digital refocusing

Digital refocusing

?

refocusing = summing windows extracted from several microlenses

15 Example of digital refocusing

Example of digital refocusing

16 Extending the depth of field

Extending the depth of field

conventional photograph, main lens at f / 4

conventional photograph, main lens at f / 22

light field, main lens at f / 4, after all-focus algorithm [Agarwala 2004]

17 Future Directions

Future Directions

Scientific Imaging Tomography, Deconvolution, Coded Aperture Imaging Computational Illumination Light stages, Domes, Light waving, Towards 8D Smart Optics Handheld Light field camera, Programmable imaging/aperture Smart Sensors HDR Cameras, Gradient Sensing, Line-scan Cameras, Demodulators Speculations

18 Novel Sensors

Novel Sensors

Color Foveon Dynamic Range HDR Camera, Log sensing Gradient sensing Identity Demodulation 3D ZCam, Canesta Motion Line scan Camera Flutter Shutter

19 Foveon: All Colors at a Single Pixel

Foveon: All Colors at a Single Pixel

20 High Dynamic Range

High Dynamic Range

http://www.cybergrain.com/tech/hdr/

Fuji's SuperCCD S3 Pro camera has a chip with high and low sensitivity sensors per pixel location to increase dynamic range

21 Gradient Camera

Gradient Camera

Sensing Pixel Intensity Difference with Locally Adaptive Gain Ramesh Raskar, MERL Work with Jack Tumblin, Northwestern U, Amit Agrawal, U of Maryland

22 High Dynamic Range Images

High Dynamic Range Images

Intensity camera fail to capture range Gradients saturate at very few isolated pixels

Scene

Intensity camera saturation map

Gradient camera saturation map

23 Natural Scene Properties

Natural Scene Properties

Intensity

Gradient

105

105

1

1

x

x

Intensity Histogram

Gradient Histogram

1

105

-105

105

24 Original Image Intensity values ranging from 0 to 1800 Intensity ramp

Original Image Intensity values ranging from 0 to 1800 Intensity ramp

plus low contrast logo

Intensity Camera Image 8 bit camera for 1:1000 range Problem: . saturation at high intensity regions

Locally Adaptive Gain Pixel divided by the average of local neighborhood. Thus the low frequency contents are lost and only detail remains.

Log Camera Image 8 bit log for 1:106 range Problem: Visible quantization effects at high intensities

Gradient Camera Image In proposed method, we sense intensity differences. We use a 8 bit A/D with resolution of log(1.02) to capture 2% contrast change between adjacent pixels. Notice that the details at both high and low intensities are captured.

25 Gradient Camera

Gradient Camera

Two main features Sense difference between neighboring pixel intensity At each pixel, measure (?x , ?y ) , ?x = Ix+1,y - Ix,y , ?y = Ix,y+1 - Ix,y With locally adaptive gain Gradient camera is very similar to locally adaptive gain camera Locally Adaptive Gain Camera Gain is different for each pixel Problem: Loses low frequency detail and preserves only high frequency features (edges) Gradient Camera The gain is same for four adjacent pixels Difference between two pixels is measured with same gain on both pixels Reconstruct original image in software from pixel differences by solving a linear system (solving Poisson Equation)

26 Camera Pipeline

Camera Pipeline

On-board Hardware

Software

Difference between pixels

Local gain adaptive to difference

2D Integration to reconstruct the image

27 Detail Preserving

Detail Preserving

Intensity cameras capture detail but lose range

Log cameras capture range but lose detail

Intensity Camera

Log Intensity Camera

Gradient Camera

28 Quantization

Quantization

Intensity Histogram

1

105

Gradient Histogram

Original Image

Uniform quantization 3 bits

-105

105

GradCam requires fewer bits In the reconstructed image, error is pushed to high gradient pixel positions which is visually imperceptible

Log Uniform quantization 3 bits

Log Uniform gradients quantization 3 bits

29 Demodulating Cameras

Demodulating Cameras

Simultaneously decode signals from blinking LEDs and get an image Sony ID Cam Phoci Motion Capture Cameras Visualeyez™ VZ4000 Tracking System PhaseSpace motion digitizer

30 D.2: Smart Optics, Modern Sensors and Future Cameras
31 Demodulating Cameras

Demodulating Cameras

Decode signals from blinking LEDs + image Sony ID Cam Phoci Motion Capture Cameras

32 3D Cameras

3D Cameras

Time of flight ZCam (Shuttered Light Pulse) Phase Decoding of modulated illumination Canesta (Phase comparison) Phase difference = depth Magnitude = reflectance Structured Light Binary coded light and triangulation

33 ZCam (3Dvsystems), Shuttered Light Pulse

ZCam (3Dvsystems), Shuttered Light Pulse

Resolution : 1cm for 2-7 meters

34 Graphics can inserted behind and between characters

Graphics can inserted behind and between characters

35 Canesta: Modulated Emitter

Canesta: Modulated Emitter

Phase ~ distance Amplitude ~ reflectance

36 Motion _ _

Motion _ _

37 Line Scan Camera: PhotoFinish 2000 Hz

Line Scan Camera: PhotoFinish 2000 Hz

38 D.2: Smart Optics, Modern Sensors and Future Cameras
39 Fluttered Shutter Camera

Fluttered Shutter Camera

Raskar, Agrawal, Tumblin Siggraph2006

40 Figure 2 results

Figure 2 results

Input Image

41 Rectified Image to make motion lines parallel to scan lines

Rectified Image to make motion lines parallel to scan lines

42 Approximate cutout of the blurred image containing the taxi

Approximate cutout of the blurred image containing the taxi

(vignetting on left edge). Exact alignment of cutout with taxi extent is not required.

Image Deblurred by solving a linear system. No post-processing

43 D.2: Smart Optics, Modern Sensors and Future Cameras
44 D.2: Smart Optics, Modern Sensors and Future Cameras
45 D.2: Smart Optics, Modern Sensors and Future Cameras
46 D.2: Smart Optics, Modern Sensors and Future Cameras
47 D.2: Smart Optics, Modern Sensors and Future Cameras
48 D.2: Smart Optics, Modern Sensors and Future Cameras
49 D.2: Smart Optics, Modern Sensors and Future Cameras
50 D.2: Smart Optics, Modern Sensors and Future Cameras
51 D.2: Smart Optics, Modern Sensors and Future Cameras
52 Novel Sensors

Novel Sensors

Color Foveon Dynamic Range HDR Camera, Log sensing Gradient sensing Identity Demodulation 3D ZCam, Canesta Motion Line scan Camera Flutter Shutter

53 Perspective

Perspective

Or Not?

Rademacher et al, MCOP, Siggraph 1998

Agrawala et al, Long Scene Panoramas, Siggraph 2006

54 Multiperspective Camera

Multiperspective Camera

[ Jingyi Yu’ 2004 ]

55 Fantasy Configurations

Fantasy Configurations

‘Cloth-cam’: ‘Wallpaper-cam’ Fusion of 4D light emission and 4D capture in the surface of a cloth… Invisible cloak Floating Cam: Ad-hoc wireless networks form camera arrays in environment… Other ray sets: Multilinear cameras (linear combination of 8 types) [Yu, McMillan’04, ’05]

56 Computational Photography

Computational Photography

Novel Cameras

Generalized Sensor

Generalized Optics

Processing

Novel Illumination

4D Incident Lighting

Ray Reconstruction

4D Ray Bender

Upto 4D Ray Sampler

4D Light Field

Display

Scene: 8D Ray Modulator

Recreate 4D Lightfield

Light Sources

Modulators

Generalized Optics

57 Goals

Goals

Capture-time Techniques Manipulating optics, illumination and sensors Fusion and Reconstruction Beyond digital darkroom experience Improving Camera Performance Better dynamic range, focus, frame rate, resolution Hint of shape, reflectance, motion and illumination Computational Imaging in Sciences Applications Graphics, Special Effects, Scene Comprehension, Art

58 Acknowledgements

Acknowledgements

MERL, Northwestern Graphics Group Amit Agrawal Shree Nayar Marc Levoy Jinbo Shi Ankit Mohan, Holger Winnemoller Image Credits Ren Ng, Vaibhav Vaish, William Bennet Fredo Durand, Aseem Agrawala Morgan McGuire, Paul Debevec And more

59 IEEE Computer Special Issue on Computational Photography

IEEE Computer Special Issue on Computational Photography

ftp://ieeecs:benefit@ftp.computer.org/mags/outgoing/computer/Aug06

Marc Levoy on "Light Fields and Computational Imaging" Shree Nayar on "Computational Cameras: Redefining the Image" Paul Debevec on "Virtual Cinematography: Relighting Through Computation" Michael F. Cohen and Richard Szeliski on "The Moment Camera" Web: www.computer.org/computer

60 Computational Photography Mastering New Techniques for Lenses,

Computational Photography Mastering New Techniques for Lenses,

Lighting and Sensors

Ramesh Raskar and Jack Tumblin Book Publishers: A K Peters Siggraph 2006 booth: 20% off Coupons 25% Off

61 Siggraph 2006 Computational Photography Papers

Siggraph 2006 Computational Photography Papers

Coded Exposure Photography: Motion Deblurring Raskar et al (MERL) Photo Tourism: Exploring Photo Collections in 3D Snavely et al (Washington) AutoCollage Rother et al (Microsoft Research Cambridge) Photographing Long Scenes With Multi-Viewpoint Panoramas Agarwala et al (University of Washington) Projection Defocus Analysis for Scene Capture and Image Display Zhang et al (Columbia University) Multiview Radial Catadioptric Imaging for Scene Capture Kuthirummal et al (Columbia University) Light Field Microscopy (Project) Levoy et al (Stanford University) Fast Separation of Direct and Global Components of a Scene Using High Frequency Illumination Nayar et al (Columbia University)

Hybrid Images Oliva et al (MIT) Drag-and-Drop Pasting Jia et al (MSRA) Two-scale Tone Management for Photographic Look Bae et al (MIT) Interactive Local Adjustment of Tonal Values Lischinski et al (Tel Aviv) Image-Based Material Editing Khan et al (Florida) Flash Matting Sun et al (Microsoft Research Asia) Natural Video Matting using Camera Arrays Joshi et al (UCSD / MERL) Removing Camera Shake From a Single Photograph Fergus (MIT)

62 Computational Photography

Computational Photography

Course WebPage http://www.merl.com/people/raskar/photo Source Code, Slides, Bibliography, Links and Updates Google: “siggraph 2006 computational photography”

63 Schedule

Schedule

8:30 Introduction (Raskar) 8:35 Photographic Signal & Film-like Photography (Tumblin) 9:15 Image Fusion and Reconstruction (Tumblin) 9:35 Computational Camera: Optics+Software (Nayar) 10:15 Break 10:30 Computational Imaging in the Sciences (Levoy) 11:10 Computational Illumination (Raskar) 11:45 Smart Optics and Sensors (Raskar) 11:45 Panel Discussion (Nayar, Levoy, Raskar, Tumblin)

Course Page : http://www.merl.com/people/raskar/photo

64 Computational Photography Panel Discussion

Computational Photography Panel Discussion

Levoy (Stanford)

Nayar (Columbia)

Raskar (MERL)

Tumblin (Northwestern)

65 Computational Photography Panel Discussion

Computational Photography Panel Discussion

66 Computational Photography Panel Discussion

Computational Photography Panel Discussion

67 Dream of A New Photography

Dream of A New Photography

Old New People and Time ~Cheap Precious Each photo Precious Free Lighting Critical Automated? External Sensors No Yes ‘Stills / Video’ Disjoint Merged Exposure Settings Pre-select Post-Process Exposure Time Pre-select Post-Process Resolution/noise Pre-select Post-Process ‘HDR’ range Pre-select Post-Process

68 Computational Photography

Computational Photography

Novel Cameras

Generalized Sensor

Generalized Optics

Processing

Novel Illumination

4D Incident Lighting

Ray Reconstruction

4D Ray Bender

Upto 4D Ray Sampler

4D Light Field

Display

Scene: 8D Ray Modulator

Recreate 4D Lightfield

Light Sources

Modulators

Generalized Optics

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