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VLSI implementation of anisotropic probabilistic neural network for real-time image scaling

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Abstract

This study proposes an VLSI implementation of anisotropic probabilistic neural network (APNN) for real-time video processing applications. The APNN interpolation method achieves good sharpness enhancement at edge regions and reveals the noise reduction at smooth region. For real-time applications, the APNN interpolation is further implemented with efficient pipelined very-large-scale integration (VLSI) architecture. The VLSI architecture of APNN has a five-layer structure, which is comprised of Euclidian layer, Gaussian layer, weighting layer, summation layer, and division layer. The VLSI implementation outperforms software with the low-loss quality. The experimental results indicate that the performance of VLSI implementation is competent for image interpolation. The presented VLSI implementation of APNN interpolation method can reach \(1920\times 1080\) at 30 frames per second (FPS) with a reasonable hardware cost.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Central University, Taoyuan County, 320, Taiwan

    Ching-Han Chen, Hsiang-Wen Chang & Chia-Ming Kuo

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  1. Ching-Han Chen
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  2. Hsiang-Wen Chang
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  3. Chia-Ming Kuo
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Correspondence to Hsiang-Wen Chang.

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Chen, CH., Chang, HW. & Kuo, CM. VLSI implementation of anisotropic probabilistic neural network for real-time image scaling. J Real-Time Image Proc 16, 71–80 (2019). https://doi.org/10.1007/s11554-018-0770-3

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  • DOI: https://doi.org/10.1007/s11554-018-0770-3

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