# The ultimate camera. Computational Photography. Creating the ultimate camera. The ultimate camera. What does it do?

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1 Computational Photography The ultimate camera What does it do? Image from Durand & Freeman s MIT Course on Computational Photography Today s reading Szeliski Chapter 9 The ultimate camera Infinite resolution Infinite zoom control Desired object(s) are in focus No noise No motion blur Infinite dynamic range (can see dark and bright things)... Creating the ultimate camera The analog camera has changed very little in >100 yrs we re unlikely to get there following this path More promising is to combine analog optics with computational techniques Computational cameras or Computational photography This lecture will survey techniques for producing higher quality images by combining optics and computation Common themes: take multiple photos modify the camera

2 Noise reduction Take several images and average them Field of view We can artificially increase the field of view by compositing several photos together (project 2). Why does this work? Basic statistics: variance of the mean decreases with n: Improving resolution: Gigapixel images Improving resolution: super resolution What if you don t have a zoom lens? Max Lyons, 2003 fused 196 telephoto shots A few other notable examples: Obama inauguration (gigapan.org) HDView (Microsoft Research)

3 Intuition (slides from Yossi Rubner & Miki Elad) Intuition (slides from Yossi Rubner & Miki Elad) For a given band-limited image, the Nyquist sampling theorem states that if a uniform sampling is fine enough ( D), perfect reconstruction is possible. D Due to our limited camera resolution, we sample using an insufficient grid D 9 10 Intuition (slides from Yossi Rubner & Miki Elad) Intuition (slides from Yossi Rubner & Miki Elad) However, if we take a second picture, shifting the camera slightly to the right we obtain: Similarly, by shifting down we get a third image: 11 12

4 Intuition (slides from Yossi Rubner & Miki Elad) Intuition And finally, by shifting down and to the right we get the fourth image: By combining all four images the desired resolution is obtained, and thus perfect reconstruction is guaranteed Example Handling more general motions 3:1 scale-up in each axis using 9 images, with pure global translation between them What if the camera displacement is Arbitrary? What if the camera rotates? Gets closer to the object (zoom)? 15 16

5 Super-resolution Basic idea: define a destination (dst) image of desired resolution assume mapping from dst to each input image is known usually a combination of a motion/warp and an average (point-spread function) can be expressed as a set of linear constraints sometimes the mapping is solved for as well add some form of regularization (e.g., smoothness assumption ) can also be expressed using linear constraints but L1, other nonlinear methods work better How does this work? [Baker & Kanade, 2002] Limits of super-resolution [Baker & Kanade, 2002] Performance degrades significantly beyond 4x or so Doesn t matter how many new images you add space of possible (ambiguous) solutions explodes quickly Major cause quantizing pixels to 8-bit gray values Dynamic Range Typical cameras have limited dynamic range Possible solutions: nonlinear techniques (e.g., L1) better priors (e.g., using domain knowledge) Baker & Kanade Hallucination, 2002 Freeman et al. Example-based super-resolution

6 HDR images merge multiple inputs HDR images merged Pixel count Pixel count Scene Radiance Radiance Camera is not a photometer! Limited dynamic range 8 bits captures only 2 orders of magnitude of light intensity We can see ~10 orders of magnitude of light intensity Unknown, nonlinear response pixel intensity amount of light (# photons, or radiance ) Solution: Recover response curve from multiple exposures, then reconstruct the radiance map Camera response function 255 Pixel value 0 log Exposure = log (Radiance * Δt) (CCD photon count)

7 Calculating response function Debevec & Malik [SIGGRAPH 1997] Δt = 1/64 sec Δt = 1/16 sec Δt = 1/4 sec Δt = 1 sec Pixel Value Z = f(exposure) Exposure = Radiance Δt log Exposure = log Radiance + log Δt Δt = 4 sec Pixel value Assuming unit radiance for each pixel log Exposure After adjusting radiances to obtain a smooth response curve Pixel value log Exposure The Math Let g(z) be the discrete inverse response function For each pixel site i in each image j, want: Solve the over-determined linear system: N P [ ln Radiance i + lnδt j g(z ij )] 2 +λ g (z) 2 i=1 j=1 ln Radiance i +ln Δt j = g(z ij ) Z max z =Z min Capture and composite several photos Same trick works for field of view resolution signal to noise dynamic range Focus But sometimes you can do better by modifying the camera fitting term smoothness term

8 Focus Suppose we want to produce images where the desired object is guaranteed to be in focus? Light field camera [Ng et al., 2005] Or suppose we want everything to be in focus? Conventional vs. light field camera Prototype camera Contax medium format camera Kodak 16-megapixel sensor Adaptive Optics microlens array 125μ square-sided microlenses pixels lenses = pixels per lens

9 Simulating depth of field Σ Σ stopping down aperture = summing only the central portion of each microlens Digital refocusing Example of digital refocusing Σ Σ refocusing = summing windows extracted from several microlenses

10 All-in-focus If you only want to produce an all-focus image, there are simpler alternatives E.g., Wavefront coding [Dowsky 1995] Coded aperture [Levin SIGGRAPH 2007], [Raskar SIGGRAPH 2007] can also produce change in focus (ala Ng s light field camera) Levin et al., SIGGRAPH 2007 Input Levin et al., SIGGRAPH 2007

11 All-focused (deconvolved) Close-up Original image All-focus image Motion blur removal Instead of coding the aperture, code the... Raskar et al., Shutter SIGGRAPH is OPEN 2007 and CLOSED

12 Raskar et al., SIGGRAPH 2007 Many more possibilities Seeing through/behind objects Using a camera array ( synthetic aperture ) Levoy et al., SIGGRAPH 2004 Removing interreflections Nayar et al., SIGGRAPH 2006 Family portraits where everyone s smiling Photomontage (Agarwala at al., SIGGRAPH 2004) License Plate Retrieval More on computational photography SIGGRAPH course notes and video Other courses MIT course CMU course Stanford course Columbia course Wikipedia page Symposium on Computational Photography ICCP 2009 (conference)

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