Paper
30 April 2010 Depth estimation, spatially variant image registration, and super-resolution using a multi-lenslet camera
Qiang Zhang, Mark Mirotznik, Santiago Saldana, Jarred Smith, Ryan Barnard
Author Affiliations +
Abstract
With a multi-lenslet camera, we can capture multiple low resolution (LR) images of the same scene and use them to reconstruct a high resolution (HR) image. For this purpose, two major computation problems need to be solved, the image registration and the super resolution (SR) reconstruction. For the first, one major hurdle is the spatially variant shifts estimation, because objects in a scene are often at different depths, and due to parallax, shifts between imaged objects often vary on a pixel basis. This poses a great computational challenge as the problem is NP complete. The multi-lenslet camera with a single focal plane provides us a unique opportunity to take advantage of the parallax phenomenon, and to directly relate object depths with their shifts, and thus we essentially reduced the parameter space from a two dimensional (x, y) space to a one dimensional depth space, which would greatly reduce the computational cost. As results, not only we have registered LR images, the estimated depth map can also be valuable for some applications. After registration, LR images along with estimated shifts can be used to reconstruct an HR image. A previously developed algorithm will be employed to efficiently compute for a large HR image in the size of 1024x1024.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qiang Zhang, Mark Mirotznik, Santiago Saldana, Jarred Smith, and Ryan Barnard "Depth estimation, spatially variant image registration, and super-resolution using a multi-lenslet camera", Proc. SPIE 7705, Modeling and Simulation for Defense Systems and Applications V, 770505 (30 April 2010); https://doi.org/10.1117/12.852171
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Cited by 1 scholarly publication and 90 patents.
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KEYWORDS
Cameras

Image registration

Lawrencium

Image resolution

Super resolution

Image segmentation

Image analysis

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