Kuwahara filter is a smoothing filter used in image processing for adaptive noise reduction that has the ability to preserve object edges. Applications for this filter exist in fields such as medical imaging and artistic imaging. However, it has a very high computational cost, especially when filter size is large. In this paper, we propose an efficient algorithm to accelerate this filter regardless of filter size. Our method uses Summed-area Table (SAT) to gain fast computation of mean and variance values in Kuwahara filter. After that, three acceleration methods, namely caching mean and variance values, memory access optimization and flexible-format SATs are proposed to optimize the SAT-based Kuwahara filter. The experiments show that our method achieves the lowest possible big-O time complexity and performs at around the same level regardless of filter size, while producing the exact same output image as the original method. The achieved speedup ratio grows quadratically with the filter size, ranging from 10x to 1000x and even more. As a result, our optimized filter can run at a high frame rate even when the filter is large.
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The authors would like to thank Vietnam Academy of Science and Technology (VAST) for funding this research in the scope of project DL0000.05/20-22.
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Le, H.D., Tran, G.S. Free-size accelerated Kuwahara filter. J Real-Time Image Proc (2021). https://doi.org/10.1007/s11554-021-01081-3
- Kuwahara filter
- Smoothing filter
- Summed-area table
- Time complexity