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Journal ArticleDOI

Pseudorandom phase masks for superresolution imaging from subpixel shifting

Amit Ashok, +1 more
- 20 Apr 2007 - 
- Vol. 46, Iss: 12, pp 2256-2268
TLDR
A method for overcoming the pixel-limited resolution of digital imagers is presented, which combines optical point-spread function engineering with subpixel image shifting and places an optimized pseudorandom phase mask in the aperture stop of a conventional imager.
Abstract
We present a method for overcoming the pixel-limited resolution of digital imagers. Our method combines optical point-spread function engineering with subpixel image shifting. We place an optimized pseudorandom phase mask in the aperture stop of a conventional imager and demonstrate the improved performance that can be achieved by combining multiple subpixel shifted images. Simulation results show that the pseudorandom phase-enhanced lens (PRPEL) imager achieves as much as 50% resolution improvement over a conventional multiframe imager. The PRPEL imager also enhances reconstruction root-mean-squared error by as much as 20%. We present experimental results that validate the predicted PRPEL imager performance.

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References
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Book

Multiuser Detection

Sergio Verdu
TL;DR: This self-contained and comprehensive book sets out the basic details of multiuser detection, starting with simple examples and progressing to state-of-the-art applications.
Journal ArticleDOI

Bayesian-Based Iterative Method of Image Restoration

TL;DR: An iterative method of restoring degraded images was developed by treating images, point spread functions, and degraded images as probability-frequency functions and by applying Bayes’s theorem.
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An iterative technique for the rectification of observed distributions

TL;DR: In this article, the authors consider the problem of determining the distribution of proper motions in a line-of-sight line of sight (LoSOS) image from a given number count.
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Fast and robust multiframe super resolution

TL;DR: This paper proposes an alternate approach using L/sub 1/ norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models and demonstrates its superiority to other super-resolution methods.
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