20 October 2015 Parallel algorithm of real-time infrared image restoration based on total variation theory
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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.
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Ran Zhu, Ran Zhu, Miao Li, Miao Li, Yunli Long, Yunli Long, Yaoyuan Zeng, Yaoyuan Zeng, Wei An, Wei An, } "Parallel algorithm of real-time infrared image restoration based on total variation theory", Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 96460Y (20 October 2015); doi: 10.1117/12.2194264; https://doi.org/10.1117/12.2194264
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