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首页> 外文会议>IEEE International Conference on Advanced Computing >Vectorization and Optimization of Fog Removal Algorithm
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Vectorization and Optimization of Fog Removal Algorithm

机译：雾移除算法的矢量化与优化

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摘要

Some of the image processing algorithms are verycostly in terms of operations and time. To use these algorithmsin real-time environment, optimization and vectorization arenecessary. In this paper, approaches are proposed to optimize, vectorize and how to fit the algorithm in low memory space. Here, optimized anisotropic diffusion based fog removal algorithm isproposed. Fog removal algorithm removes the fog from imageand produces an image having better visibility. This algorithmhas many phases like anisotropic diffusion, histogram stretchingand smoothing. Anisotropic diffusion is an iterative process thattakes nearly 70% of time complexity of the whole algorithm. Here, optimization and vectorization of the anisotropic diffusion is proposed for better performance. However, optimizationtechniques cost some accuracy but that can be neglected forsignificant improvement in performance. For memory constraintenvironment, a method is proposed to process the entire blockof image and maintains the integrity of operations. Resultsconfirm that with our optimization and vectorization approaches, performance is increased up to 90 fps (approximately) for VGAimage on one of the image processing DSP simulator. Even if, system doesn't have vector operations, the proposed optimizationtechniques can be used to achieve better performance (2× faster).
机译：一些图像处理算法在操作和时间方面非常稳定。使用这些算法的实时环境，优化和矢量化。在本文中，提出了方法来优化，矢量化和如何在低存储空间中拟合算法。这里，优化的各向异性扩散基雾去除算法缺失。雾拆卸算法从Imageand中删除雾生成具有更好可见性的图像。这种算法许多相似的各向异性扩散，直方图拉伸和平滑。各向异性扩散是一种迭代过程，当时整个算法的时间复杂度近70％。这里，提出了各向异性扩散的优化和升值，以便更好的性能。然而，优化技术成本一些准确性，但可以忽视性能方面的显着改善。对于内存约束环境，提出了一种方法来处理整个块图像并保持操作的完整性。结果截止数据，通过我们的优化和矢量化方法，对于其中一个图像处理DSP模拟器上的VGaimage，性能增加到90 fps（大约）。即使系统没有矢量操作，也可以使用所提出的优化技术来实现更好的性能（2×更快）。

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