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Proceedings Paper

Parallel algorithm of real-time infrared image restoration based on total variation theory
Author(s): Ran Zhu; Miao Li; Yunli Long; Yaoyuan Zeng; Wei An
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Paper Abstract

Image restoration is a necessary preprocessing step for infrared remote sensing applications. Traditional methods allow us to remove the noise but penalize too much the gradients corresponding to edges. Image restoration techniques based on variational approaches can solve this over-smoothing problem for the merits of their well-defined mathematical modeling of the restore procedure. The total variation (TV) of infrared image is introduced as a L1 regularization term added to the objective energy functional. It converts the restoration process to an optimization problem of functional involving a fidelity term to the image data plus a regularization term. Infrared image restoration technology with TV-L1 model exploits the remote sensing data obtained sufficiently and preserves information at edges caused by clouds. Numerical implementation algorithm is presented in detail. Analysis indicates that the structure of this algorithm can be easily implemented in parallelization. Therefore a parallel implementation of the TV-L1 filter based on multicore architecture with shared memory is proposed for infrared real-time remote sensing systems. Massive computation of image data is performed in parallel by cooperating threads running simultaneously on multiple cores. Several groups of synthetic infrared image data are used to validate the feasibility and effectiveness of the proposed parallel algorithm. Quantitative analysis of measuring the restored image quality compared to input image is presented. Experiment results show that the TV-L1 filter can restore the varying background image reasonably, and that its performance can achieve the requirement of real-time image processing.

Paper Details

Date Published: 20 October 2015
PDF: 7 pages
Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 96460Y (20 October 2015); doi: 10.1117/12.2194264
Show Author Affiliations
Ran Zhu, National Univ. of Defense Technology (China)
Miao Li, National Univ. of Defense Technology (China)
Yunli Long, National Univ. of Defense Technology (China)
Yaoyuan Zeng, National Univ. of Defense Technology (China)
Wei An, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 9646:
High-Performance Computing in Remote Sensing V
Bormin Huang; Sebastián López; Zhensen Wu; Jose M. Nascimento; Boris A. Alpatov; Jordi Portell de Mora, Editor(s)

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