23 September 2014 Real time soft-partition-based weighted sum filtering with GPU acceleration
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Recently image processing such as noise reduction, restoration, and super-resolution using the soft-partition-based weighted sum filters have shown state-of-the-art results. The partition-based weighted sum filters are spatially adaptive filtering techniques by combining vector quantization and linear finite impulse response filtering, which have been shown to achieve much better results than spatial-invariant filtering methods. However, they are computationally prohibitive for practical applications because of enormous computation involved in both filtering and training. Real-time filtering is impossible even for small image and window sizes. This paper presents fast implementations of the soft-partition-based weighted sum filtering by exploiting the massively parallel processing capabilities of a GPU within the CUDA framework. For the implementations, we focus on memory management and implementation strategies. The performance on various image and window sizes is measured and compared between the GPU-based and CPU-based implementations. The results show that the GPU-based implementations can significantly accelerate computations for the soft-partition-based weighted sum filtering, and make real-time image filtering possible.
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Shuqun Zhang, Shuqun Zhang, Bryan Furia, Bryan Furia, "Real time soft-partition-based weighted sum filtering with GPU acceleration", Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 921723 (23 September 2014); doi: 10.1117/12.2062478; https://doi.org/10.1117/12.2062478

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