# Fourier Slice Photography

## Presentation on theme: "Fourier Slice Photography"— Presentation transcript:

Fourier Slice Photography
Ren Ng Stanford University

Conventional Photograph
Okay, so here’s the conventional photograph that we would have gotten. Notice that it’s focused on Matt, and that Jacquie and Matt are out of focus.

Light Field Photography
Capture the light field inside the camera body Okay, the first thing that I want to do is define this “light field” term that I’ve been using. The light field is just a representation for the light traveling along all rays in free space. Inside the camera, we can parameterize all the rays by where they originate on the lens plane, and where they terminate on the sensor. So the light traveling from (u,v) on the lens to (x,y) on the sensor is given by L(u,v,x,y). Note that this is a four dimensional function – the space of rays is 4D. The concept of the light field is the second most cited idea in computer graphics. It was introduced in 1996 by Marc and Pat at Stanford, and by Steve Gortler and colleagues at Harvard and Microsoft Research.

Hand-Held Light Field Camera
Medium format digital camera Camera in-use 16 megapixel sensor Microlens array

Light Field in a Single Exposure

Light Field in a Single Exposure

Light Field Inside the Camera Body
Okay, the first thing that I want to do is define this “light field” term that I’ve been using. The light field is just a representation for the light traveling along all rays in free space. Inside the camera, we can parameterize all the rays by where they originate on the lens plane, and where they terminate on the sensor. So the light traveling from (u,v) on the lens to (x,y) on the sensor is given by L(u,v,x,y). Note that this is a four dimensional function – the space of rays is 4D. The concept of the light field is the second most cited idea in computer graphics. It was introduced in 1996 by Marc and Pat at Stanford, and by Steve Gortler and colleagues at Harvard and Microsoft Research.

Digital Refocusing Okay, so here’s the conventional photograph that we would have gotten. Notice that it’s focused on Matt, and that Jacquie and Matt are out of focus.

Digital Refocusing But by simulating the light light… etc…

What is the computational complexity? Are there efficient algorithms? What are the limits on refocusing? How far can we move the focal plane?

Overview Fourier Slice Photography Theorem
Fourier Refocusing Algorithm Theoretical Limits of Refocusing Here’s an overview for what I’ll be talking about in this section. First, I’d like to go over the derivation of the theorem at the heart of this whole analytic approach. The theorem says that in the Fourier domain, photographs are just 2D slice in the 4D light field. That’s much simpler than in the spatial-domain, where photographs are integral projections of the light field. Then I want to show you how the theorem can be applied to make theoretical analysis of our camera easier, and how it gives us a fast algorithm for digital refocusing.

Previous Work Integral photography Lippmann 1908, Ives 1930
Lots of variants, especially in 3D TV Okoshi 1976, Javidi & Okano 2002 Closest variant is plenoptic camera Adelson & Wang 1992 Fourier analysis of light fields Chai et al. 2000 Refocusing from light fields Isaksen et al. 2000, Stewart et al. 2003

Fourier Slice Photography Theorem
In the Fourier domain, a photograph is a 2D slice in the 4D light field. Photographs focused at different depths correspond to 2D slices at different trajectories.

Digital Refocusing by Ray-Tracing
x Lens Sensor

Digital Refocusing by Ray-Tracing
x Imaginary film Lens Sensor

Digital Refocusing by Ray-Tracing
x Imaginary film Lens Sensor

Digital Refocusing by Ray-Tracing
x Imaginary film Lens Sensor

Digital Refocusing by Ray-Tracing
x Imaginary film Lens Sensor

Refocusing as Integral Projection
x u u x Imaginary film Lens Sensor

Refocusing as Integral Projection
x u u x Imaginary film Lens Sensor

Refocusing as Integral Projection
x u u x Imaginary film Lens Sensor

Refocusing as Integral Projection
x u u x Imaginary film Lens Sensor

Classical Fourier Slice Theorem
Integral Projection 2D Fourier Transform 1D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Classical Fourier Slice Theorem
Integral Projection 2D Fourier Transform 1D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Classical Fourier Slice Theorem
Integral Projection 2D Fourier Transform 1D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Classical Fourier Slice Theorem
Integral Projection Spatial Domain Fourier Domain Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Classical Fourier Slice Theorem
Integral Projection Spatial Domain Fourier Domain Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Fourier Slice Photography Theorem
Integral Projection Spatial Domain Fourier Domain Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Fourier Slice Photography Theorem
Integral Projection 4D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Fourier Slice Photography Theorem
Integral Projection 4D Fourier Transform 2D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Fourier Slice Photography Theorem
Integral Projection 4D Fourier Transform 2D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Fourier Slice Photography Theorem
Integral Projection 4D Fourier Transform 2D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Photographic Imaging Equations
Spatial-Domain Integral Projection Fourier-Domain Slicing So here are the definitions of these operators. We already saw in the spatial domain that imaging is this double integral. And in the Fourier domain, here’s the definition of the slicing operator, which shows that the value at any point in the photograph is given by the value at a corresponding point in the light field. So I think this makes really clear why this theorem is useful. On the theoretical side, if you have to do any math, it’s much nicer to work with the Fourier definition where you don’t have to deal with the integral symbols. And on the practical side, if you have to do any computation, it might be better to work in the Fourier domain, where you can just look up an array element rather than summing a bunch of array elements. 10 minutes to here

Photographic Imaging Equations
Spatial-Domain Integral Projection Fourier-Domain Slicing So here are the definitions of these operators. We already saw in the spatial domain that imaging is this double integral. And in the Fourier domain, here’s the definition of the slicing operator, which shows that the value at any point in the photograph is given by the value at a corresponding point in the light field. So I think this makes really clear why this theorem is useful. On the theoretical side, if you have to do any math, it’s much nicer to work with the Fourier definition where you don’t have to deal with the integral symbols. And on the practical side, if you have to do any computation, it might be better to work in the Fourier domain, where you can just look up an array element rather than summing a bunch of array elements. 10 minutes to here

Photographic Imaging Equations
Spatial-Domain Integral Projection Fourier-Domain Slicing So here are the definitions of these operators. We already saw in the spatial domain that imaging is this double integral. And in the Fourier domain, here’s the definition of the slicing operator, which shows that the value at any point in the photograph is given by the value at a corresponding point in the light field. So I think this makes really clear why this theorem is useful. On the theoretical side, if you have to do any math, it’s much nicer to work with the Fourier definition where you don’t have to deal with the integral symbols. And on the practical side, if you have to do any computation, it might be better to work in the Fourier domain, where you can just look up an array element rather than summing a bunch of array elements. 10 minutes to here

Theorem Limitations Film parallel to lens
Everyday camera, not view camera Aperture fully open Closing aperture requires spatial mask

Overview Fourier Slice Photography Theorem
Fourier Refocusing Algorithm Theoretical Limits of Refocusing Here’s an overview for what I’ll be talking about in this section. First, I’d like to go over the derivation of the theorem at the heart of this whole analytic approach. The theorem says that in the Fourier domain, photographs are just 2D slice in the 4D light field. That’s much simpler than in the spatial-domain, where photographs are integral projections of the light field. Then I want to show you how the theorem can be applied to make theoretical analysis of our camera easier, and how it gives us a fast algorithm for digital refocusing.

Existing Refocusing Algorithms
Existing refocusing algorithms are expensive O(N4) where light field has N samples in each dimension All are variants on integral projection Isaksen et al Vaish et al Levoy et al Ng et al The point I want to make here is that all existing digital refocusing approaches are variants on spatial domain integration, from the original paper demonstrating refocusing by Isaksen, McMillan and Gortler in 2000, through work on synthetic photography at Stanford i the last two years. The point here is the digital refocusing is expensive – O(N^4) if we have N samples in each dimension – because to compute any photograph we have to project and integrate the entire light field.

Refocusing in Spatial Domain
Integral Projection 4D Fourier Transform 2D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Refocusing in Fourier Domain
Integral Projection Inverse 2D Fourier Transform 4D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Refocusing in Fourier Domain
Integral Projection Inverse 2D Fourier Transform 4D Fourier Transform Okay so here’s the diagram for the Fourier Slice Photography Theorem again. So all existing approaches compute the refocused photograph from the light field by working in the spatial domain. The idea for a faster algorithm is to work in the FOurier domain. Slicing

Asymptotic Performance
Fourier-domain slicing algorithm Pre-process: O(N4 log N) Refocusing: O(N2 log N) Spatial-domain integration algorithm Refocusing: O(N4) So the asymptotic performance in the Fourier domain is O(N4 log N) for the pre-process, and O(N2 log N), dominated by the inverse 2D transform. And of course that’s to be compared against the spatial domain algorithm, which is O(N4) for each refocusing step. I also want to mention that practically, the absolute performance of the two algorithms is about the same for resolutions captured with our prototype. At directional resolutions twice as high, Fourier algorithm is order of magnitude faster.

Resampling Filter Choice
Kaiser-Bessel filter (width 2.5) Gold standard (spatial integration) Triangle filter (quadrilinear)

Overview Fourier Slice Photography Theorem
Fourier Refocusing Algorithm Theoretical Limits of Refocusing Here’s an overview for what I’ll be talking about in this section. First, I’d like to go over the derivation of the theorem at the heart of this whole analytic approach. The theorem says that in the Fourier domain, photographs are just 2D slice in the 4D light field. That’s much simpler than in the spatial-domain, where photographs are integral projections of the light field. Then I want to show you how the theorem can be applied to make theoretical analysis of our camera easier, and how it gives us a fast algorithm for digital refocusing.

Problem Statement Assume a light field camera with An f /A lens
N x N pixels under each microlens If we compute refocused photographs from these light fields, over what range can we move the focal plane? Analytical assumption Assume band-limited light fields

Band-Limited Analysis
Okay, so if what do we get with this band-limited assumption? Well, here is the continuous situation. This shows the Fourier transform of the continuous light field in 2D, which is in general unbounded, and here is the optical photograph that forms at a particular focal depth: it’s just a slice in the light field. The conventional camera band-limits this continuous photograph by cutting off its high frequencies, and if we focus at a different depth, getting a different slice, the band-limit is the same. In the plenoptic camera, the band-limit isn’t directly on the photo, but rather on the light field. So we cut off the high frequencies in the light field. Now, when we produce a photograph with digital refocusing, we extract the slice here, but the slice is clipped to the bounds of the light field. If we refocus at a different depth, the width of the bandwidth changes. One thing to note is that the refocused photographs is just a band-limited version of the continuous photograph. By comparing the bandwidths, we can tell how close digital refocusing is getting us to the ideal result.

Band-Limited Analysis
Band-width of measured light field Okay, so if what do we get with this band-limited assumption? Well, here is the continuous situation. This shows the Fourier transform of the continuous light field in 2D, which is in general unbounded, and here is the optical photograph that forms at a particular focal depth: it’s just a slice in the light field. The conventional camera band-limits this continuous photograph by cutting off its high frequencies, and if we focus at a different depth, getting a different slice, the band-limit is the same. In the plenoptic camera, the band-limit isn’t directly on the photo, but rather on the light field. So we cut off the high frequencies in the light field. Now, when we produce a photograph with digital refocusing, we extract the slice here, but the slice is clipped to the bounds of the light field. If we refocus at a different depth, the width of the bandwidth changes. One thing to note is that the refocused photographs is just a band-limited version of the continuous photograph. By comparing the bandwidths, we can tell how close digital refocusing is getting us to the ideal result. Light field shot with camera

Band-Limited Analysis
Okay, so what do we get with this band-limited assumption? Well, here is the continuous situation. This shows the Fourier transform of the continuous light field in 2D, which is in general unbounded, and here is the optical photograph that forms at a particular focal depth: it’s just a slice in the light field. The conventional camera band-limits this continuous photograph by cutting off its high frequencies, and if we focus at a different depth, getting a different slice, the band-limit is the same. In the plenoptic camera, the band-limit isn’t directly on the photo, but rather on the light field. So we cut off the high frequencies in the light field. Now, when we produce a photograph with digital refocusing, we extract the slice here, but the slice is clipped to the bounds of the light field. If we refocus at a different depth, the width of the bandwidth changes. One thing to note is that the refocused photographs is just a band-limited version of the continuous photograph. By comparing the bandwidths, we can tell how close digital refocusing is getting us to the ideal result.

Band-Limited Analysis
Okay, so if what do we get with this band-limited assumption? Well, here is the continuous situation. This shows the Fourier transform of the continuous light field in 2D, which is in general unbounded, and here is the optical photograph that forms at a particular focal depth: it’s just a slice in the light field. The conventional camera band-limits this continuous photograph by cutting off its high frequencies, and if we focus at a different depth, getting a different slice, the band-limit is the same. In the plenoptic camera, the band-limit isn’t directly on the photo, but rather on the light field. So we cut off the high frequencies in the light field. Now, when we produce a photograph with digital refocusing, we extract the slice here, but the slice is clipped to the bounds of the light field. If we refocus at a different depth, the width of the bandwidth changes. One thing to note is that the refocused photographs is just a band-limited version of the continuous photograph. By comparing the bandwidths, we can tell how close digital refocusing is getting us to the ideal result.

Band-Limited Analysis
Okay, so if what do we get with this band-limited assumption? Well, here is the continuous situation. This shows the Fourier transform of the continuous light field in 2D, which is in general unbounded, and here is the optical photograph that forms at a particular focal depth: it’s just a slice in the light field. The conventional camera band-limits this continuous photograph by cutting off its high frequencies, and if we focus at a different depth, getting a different slice, the band-limit is the same. In the plenoptic camera, the band-limit isn’t directly on the photo, but rather on the light field. So we cut off the high frequencies in the light field. Now, when we produce a photograph with digital refocusing, we extract the slice here, but the slice is clipped to the bounds of the light field. If we refocus at a different depth, the width of the bandwidth changes. One thing to note is that the refocused photographs is just a band-limited version of the continuous photograph. By comparing the bandwidths, we can tell how close digital refocusing is getting us to the ideal result.

Photographic Imaging Equations
Spatial-Domain Integral Projection Fourier-Domain Slicing So here are the definitions of these operators. We already saw in the spatial domain that imaging is this double integral. And in the Fourier domain, here’s the definition of the slicing operator, which shows that the value at any point in the photograph is given by the value at a corresponding point in the light field. So I think this makes really clear why this theorem is useful. On the theoretical side, if you have to do any math, it’s much nicer to work with the Fourier definition where you don’t have to deal with the integral symbols. And on the practical side, if you have to do any computation, it might be better to work in the Fourier domain, where you can just look up an array element rather than summing a bunch of array elements. 10 minutes to here

Results of Band-Limited Analysis
Assume a light field camera with An f /A lens N x N pixels under each microlens From its light fields we can Refocus exactly within depth of field of an f /(A  N) lens In our prototype camera Lens is f /4 12 x 12 pixels under each microlens Theoretically refocus within depth of field of an f/48 lens

Light Field Photo Gallery
Okay, now some more photographic results.

Rodin’s Burghers of Calais

Palace of Fine Arts, San Francisco

Palace of Fine Arts, San Francisco

Waiting to Race

Start of the Race

Summary of Main Contributions
Formal theorem about relationship between light fields and photographs Computational application gives asymptotically fast refocusing algorithm Theoretical application gives analytic solution for limits of refocusing

Future Work Apply general signal-processing techniques
Cross-fertilization with medical imaging

Thanks and Acknowledgments
Collaborators on camera tech report Marc Levoy, Mathieu Brédif, Gene Duval, Mark Horowitz and Pat Hanrahan Readers and listeners Ravi Ramamoorthi, Brian Curless, Kayvon Fatahalian, Dwight Nishimura, Brad Osgood, Mike Cammarano, Vaibhav Vaish, Billy Chen, Gaurav Garg, Jeff Klingner Anonymous SIGGRAPH reviewers Funding sources NSF, Microsoft Research Fellowship, Stanford Birdseed Grant

Questions? “Start of the race”, Stanford University Avery Pool, July 2005