Vectorization and Optimization of Fog Removal Algorithm

Vectorization and Optimization of Fog Removal Algorithm

Abstract:

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).

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