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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 13 — May. 1, 2014
  • pp: C1–C13

Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter

Michael Rucci, Russell C. Hardie, and Kenneth J. Barnard  »View Author Affiliations


Applied Optics, Vol. 53, Issue 13, pp. C1-C13 (2014)
http://dx.doi.org/10.1364/AO.53.0000C1


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Abstract

In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.

© 2014 Optical Society of America

OCIS Codes
(100.1830) Image processing : Deconvolution
(100.2000) Image processing : Digital image processing
(100.3020) Image processing : Image reconstruction-restoration

History
Original Manuscript: December 9, 2013
Revised Manuscript: February 23, 2014
Manuscript Accepted: March 3, 2014
Published: April 11, 2014

Citation
Michael Rucci, Russell C. Hardie, and Kenneth J. Barnard, "Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter," Appl. Opt. 53, C1-C13 (2014)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-13-C1

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Supplementary Material


» Media 1: MOV (4241 KB)     
» Media 2: MOV (4385 KB)     
» Media 3: MOV (3808 KB)     
» Media 4: MOV (3770 KB)     
» Media 5: MOV (2651 KB)     
» Media 6: MOV (3906 KB)     
» Media 7: MOV (4527 KB)     

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