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.
As one of the most significant methods to study laser propelled rocket, the numerical simulation of laser propulsion has drawn an ever increasing attention at present. Nevertheless, the traditional serial simulation model cannot satisfy the practical needs because of insatiable memory overhead and considerable computation time. In order to solve this problem, we study on a general algorithm for laser propulsion design, and bring about parallelization by using a twolevel hybrid parallel programming model. The total computing domain is decomposed into distributed data spaces, and each partition is assigned to a MPI process. A single step of computation operates in the inter loop level, where a compiler directive is used to split MPI process into several OpenMP threads. Finally, parallel efficiency of hybrid program about two typical configurations on a China-made supercomputer with 4 to 256 cores is compared with pure MPI program. And, the hybrid program exhibits better performance than the pure MPI program on the whole, roughly as expected. The result indicates that our hybrid parallel approach is effective and practical in large-scale numerical simulation of laser propulsion.