Raskar Paris Nov08

Raskar, Camera Culture, MIT Media Lab

Camera Culture

Ramesh Raskar

Camera CultureAssociate Professor, MIT Media Lab

Computational Photography:

Epsilon to Coded Imaging

Where are the ‘camera’s?Where are the ‘camera’s?

We focus on creating tools to better capture and share visual information

The goal is to create an entirely new class of imaging platforms

that have an understanding of the world that far exceeds human ability

and produce meaningful abstractions that are well within human comprehensibility

Ramesh Raskar


Cameras of Tomorrow

Motion Blurred Photo

Flutter Shutter CameraFlutter Shutter CameraRaskar, Agrawal, Tumblin [Siggraph2006]

LCD opacity switched in coded sequence

Short Exposure

Traditional MURA Coded

Deblurred Result

Captured SinglePhoto

Shutter

Banding Artifacts and some spatial frequencies

are lostDark

and noisy

Blurring == Convolution

Traditional Camera: Shutter is OPEN: Box Filter

PSF == Sinc Function

ω

Sharp Photo

Blurred Photo

Fourier Transform

Flutter Shutter: Shutter is OPEN and CLOSED

Preserves High Spatial Frequencies

Sharp Photo

Blurred PhotoPSF == Broadband Function

Fourier Transform

Traditional

Coded Exposu

re

Image of Static Object

Deblurred Image

Deblurred Image

Coded Exposure

Temporal 1-D broadband code: Motion Deblurring

Coded Aperture

Spatial 2-D broadband mask: Focus Deblurring

Coded Aperture CameraCoded Aperture Camera

The aperture of a 100 mm lens is modified

Rest of the camera is unmodifiedInsert a coded mask with chosen binary pattern

In Focus Photo

LED

Out of Focus Photo: Open Aperture

Out of Focus Photo: Coded Aperture

Captured Blurred Photo

Refocused on Person

Less is MoreLess is MoreBlocking Light == More InformationBlocking Light == More Information

Coding in Time Coding in Time Coding in SpaceCoding in Space

Larval Trematode WormLarval Trematode Worm Coded Aperture CameraCoded Aperture Camera

Shielding Light …Shielding Light …

Larval Trematode WormLarval Trematode Worm Turbellarian WormTurbellarian Worm

Coded Computational Photography• Coded Exposure

– Motion Deblurring [2006]

• Coded Aperture– Focus Deblurring [2007]– Glare reduction [2008]

• Optical Heterodyning– Light Field Capture [2007]

• Coded Illumination– Motion Capture [2007]– Multi-flash: Shape Contours [2004]

• Coded Spectrum– Agile Wavelength Profile [2008]

• Epsilon->Coded->Essence Photographyhttp://raskar.info


Computational Photography

1. Epsilon Photography– Low-level Vision: Pixels– Multiphotos by bracketing (HDR, panorama)– ‘Ultimate camera’

2. Coded Photography– Mid-Level Cues:

• Regions, Edges, Motion, Direct/global– Single/few snapshot

• Reversible encoding of data– Additional sensors/optics/illum

3. Essence Photography– Not mimic human eye– Beyond single view/illum– ‘New artform’


Epsilon Photography

• Dynamic range– Exposure braketing [Mann-Picard, Debevec]

• Wider FoV – Stitching a panorama

• Depth of field – Fusion of photos with limited DoF [Agrawala04]

• Noise– Flash/no-flash image pairs [Petschnigg04, Eisemann04]

• Frame rate– Triggering multiple cameras [Wilburn05, Shechtman02]


• 3D– Stereo of multiple cameras

• Higher dimensional LF– Light Field Capture

• lenslet array [Adelson92, Ng05], ‘3D lens’ [Georgiev05], heterodyne masks [Veeraraghavan07]

• Boundaries and Regions– Multi-flash camera with shadows [Raskar08]

– Fg/bg matting [Chuang01,Sun06]

• Deblurring– Engineered PSF– Motion: Flutter shutter[Raskar06], Camera Motion [Levin08]

– Defocus: Coded aperture, Wavefront coding [Cathey95]

• Global vs direct illumination– High frequency illumination [Nayar06]

– Glare decomposition [Talvala07, Raskar08]

• Coded Sensor– Gradient camera [Tumblin05]


• Ramesh Raskar and Jack Tumblin

• Book Publishers: A K Peters

Mask?

Sensor

MaskSensorMask

?Sensor

MaskSensor

Mask?

Sensor

4D Light Field from 2D Photo:

Heterodyne Light Field Camera

Full Resolution Digital Refocusing:

Coded Aperture Camera

Lenslet-based Light Field cameraLenslet-based Light Field camera

[Adelson and Wang, 1992, Ng et al. 2005 ]

Light Field Inside a CameraLight Field Inside a Camera

Stanford Plenoptic Camera Stanford Plenoptic Camera [Ng et al 2005][Ng et al 2005]

4000 × 4000 pixels ÷ 292 × 292 lenses = 14 × 14 pixels per lens

Contax medium format camera Kodak 16-megapixel sensor

Adaptive Optics microlens array 125μ square-sided microlenses

Digital RefocusingDigital Refocusing

[Ng et al 2005][Ng et al 2005]

Can we achieve this with a Can we achieve this with a MaskMask alone? alone?

Mask based Light Field CameraMask Sensor

[Veeraraghavan, Raskar, Agrawal, Tumblin, Mohan, Siggraph 2007 ]

How to Capture 4D Light Field with 2D

Sensor ?

What should be the pattern of the mask ?

Optical HeterodyningOptical Heterodyning

Photographic Signal

(Light Field)

Carrier Incident Modulat

ed Signal

ReferenceCarrier

Main LensObject Mask Sensor

RecoveredLight Field

Software Demodulation

Baseband Audio Signal

Receiver: Demodulation

High Freq Carrier 100 MHz

ReferenceCarrier

Incoming Signal

99 MHz

1/f0

Mask Tile

Cosine Mask Used

Captured 2D Photo

Encoding due to Mask

2D FFT

Traditional Camera Photo

Heterodyne Camera Photo

Magnitude of 2D FFT

2D FFT

Magnitude of 2D FFT

Computing 4D Light Field2D Sensor Photo, 1800*1800 2D Fourier Transform, 1800*1800

2D FFT

Rearrange 2D tiles into 4D planes200*200*9*94D IFFT

4D Light Field

9*9=81 spectral copies

200*200*9*9

Extra sensor bandwidth cannot capture extra dimension of the light field

fθ

fx

fθ0

fx0

Sensor Slice

Extra sensor bandwidth

Fourier Light Field Space

Solution: Modulation Theorem

Make spectral copies of 2D light field

fθ

fx

fθ0

fx0

Modulation Function

fθ

Modulated Light Field

fx

fθ0

fx0

Modulation Function

Sensor Slice captures entire Light Field

Demodulation to recover Light Field

fθ

fx

Reshape 1D Fourier Transform into 2D

1D Fourier Transform of Sensor Signal

Full resolution 2D image of Focused Scene Parts

Captured 2D Photo

Image of White Lambertian Plane

divide

MIT Media Lab

Single shot visual hull: Shield Field

Lanman, Raskar, Agrawal, Taubin, Siggraph Asia 2008

MIT Media Lab

Single shot 3D reconstruction: Simultaneous Projections using Masks

• Barcode size : 3mm x 3mm• Distance from camera : 5 meter

Long Distance Bar-codesLong Distance Bar-codes

Woo, Mohan, Raskar, [2008]

Mitsubishi Electric Research Laboratories Raskar 2006Spatial Augmented Reality

Computational Camera and PhotographyComputational Camera and Photography

Towards a 6D DisplayPassive Reflectance Field Display

Martin Fuchs, Ramesh Raskar,Hans-Peter Seidel, Hendrik P. A. Lensch

Siggraph 2008

1 2

11

1 MPI Informatik, Germany 2 MIT

2D 2D 2D

View dependent 4D Display

6D = light sensitive 4D display

One Pixel of a 6D Display = 4D Display

Camera Culture group

• New group at Media Lab– New graduate student RA, post-doc, MEng slot open– Graduate school deadline Dec 15th

– New faculty positions

• Keywords– Computational imaging, Signal processing, Applied optics, Computer graphics and vision,

Hardware electronics, Displays, Art, Online photo collections, Visual social computing

• Acknowledgements– My former group at MERL, Amit Agrawal, Jack Tumblin, Shree Nayar– ETVC: Frank, Evelyne– Apologies for not covering related work

http://CameraCulture.info

Fernald, Science [Sept 2006]

Shadow Refractive

Reflective

Tools for

Visual Computin

g

Blind CameraBlind Camera

Sascha Pohflepp, U of the Art, Berlin, 2006


Cameras of Tomorrow

Cameras of Tomorrow• Coded Exposure

– Motion Deblurring [2006]

• Coded Aperture– Focus Deblurring [2007]– Glare reduction [2008]

• Optical Heterodyning– Light Field Capture [2007]

• Coded Illumination– Motion Capture [2007]– Multi-flash: Shape Contours [2004]

• Coded Spectrum– Agile Wavelength Profile [2008]

• Epsilon->Coded->Essence Photographyhttp://raskar.info

We focus on creating tools to better capture and share visual information

The goal is to create an entirely new class of imaging platforms

that have an understanding of the world that far exceeds human ability

and produce meaningful abstractions that are well within human comprehensibility

Ramesh Raskar http://raskar.info

Raskar Paris Nov08

Art & Photos

Transcript of Raskar Paris Nov08