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CSE 597F : Computational Photography
Spring 2010 Fridays 1:25-3:55 in 002 DeikeInstructors: R.Collins, D.Capel and Y.Liu
Credits: 3 (will count as a 598 for fulfilling graduation requirements).
Course Description
• Computational photography combines elements of optics, graphics, and computer vision to enhance or extend the capabilities of digital photography.
• We will learn about this new visual medium through lectures, readings and hands-on photographic assignments, culminating in a final course project.
Prerequisites
• knowledge of Matlab; • either CMPEN454 (Vision) or 455 (Image Processing)
or equivalents;
The following are helpful but not required:• CMPSC 458 (Computer Graphics); • access to a digital camera that allows manual control
of shutter and aperture.
Image Alignment and Mosaicing
• sample paper: Brown and Lowe. “Automatic Panoramic Image Stitching using Invariant Features “, IJCV 2006.
Image Alignment and Mosaicing
gain compensation and blending
automatic alignment of photo collections
Pyramid-based Blending
= +
+
+
+
Laplacian Image Pyramid
• sample paper: Burt and Adelson, “A multiresolutionspline with application to image mosaics,” ACM Trans on Graphics, 1983.
Gradient-Domain Blendingimage compositing by “cloning” and blending
• sample paper: Perez et.al. “Poisson Image Editing”, Siggraph 2003.
Tone Mapping and HDR Imaging
overexposed
underexposed collect a range of exposures
• sample paper: Debevec and Malik. “Recovering High Dynamic Range Radiance Maps from Photographs.” Siggraph 1997.
Flash / No-Flash Photography
• sample paper: Petschnigg et.al., “Digital Photography with Flash and No-Flash Image Pairs.”, Siggraph 2004.
low light picture, no flash- very noisy- warm natural lighting
picture with flash- less noise, more detail- but cold and unnatural lighting
Flash / No-Flash Photography
combined image- good smoothing- details transferred from flash- retains the warm, natural lighting
Coded-Aperture Imaging
• sample paper: Levin et.al., “Image and Depth from a Conventional Camera with a Coded Aperture”, Siggraph 2007.
depth estimationfrom single image
deblurring /refocusing
Graphics and Photography• Seam-carving
• sample paper: Avidan and Shamir, “Seam Carving for Content-Aware Image Resizing”, Siggraph 2007.
image resizing
object removal
Graphics and Photography• Delayering and
Inpainting
de-fencing; Liu et.al.
image restoration
• Sample papers: Liu et.al., “Image Defencing”, CVPR 2008; “Near-regular texture analysis and manipulation”, Siggraph2004; “A Lattice-based MRF Model for Dynamic Near-regular Texture Tracking,” PAMI 2007.
Graphics and Photography
• sample paper: Levin et.al., “Colorization using Optimization,” ACM Transactions on Graphics, Aug 2004
greyscale photo with user-annotated colors colorized result
• Colorization
• Texture transfer and Image analogies
• sample paper: Hertzmann et.al., “Image Analogies,” Siggraph2001
Graphics and Photography