一个基于高效FPGA实施的硬件友好的联合剥离和拆解系统.
Jiqing Wang1, Xiang Wang1, Yu Shen1
1School of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Micromachines
|January 28, 2026
概括
这项研究介绍了一种高效的硬件系统,用于联合图像去色和去色化. 这种新的方法显著降低了模型的复杂性,提高了图像质量,为实时应用提供了实用的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 硬件加速器 硬件加速器
- 图像处理 图像处理
背景情况:
- 图像采集经常受到噪音和颜色波器阵列工件的影响.
- 对于实时硬件实现,现有的除和拆解方法可能缺乏效率.
- 开发综合解决方案对于提高图像质量和处理速度至关重要.
研究的目的:
- 设计一个硬件可加速的系统,用于联合去噪和去.
- 为高效的图像处理提出一个轻量级的神经网络架构.
- 为灵活加速开发一个统一的硬件平台.
主要方法:
- 开发了一种轻量级的网络架构,利用部分卷积和多尺度特征提取.
- 使用可分离和部分卷积来最大限度地减少模型参数和计算.
- 在Xilinx Zynq UltraScale+ FPGA上实现了一个可配置的硬件加速平台.
主要成果:
- 与标准卷曲相比,拟议的方法实现了参数 (83.38%) 和MAC (77.71%) 的显著降低.
- 在Kodak24数据集上,观察到PSNR的2.36dB和SSIM的0.0806的改善.
- 计算效率提高了2.09x,硬件架构支持多并行.
结论:
- 拟议的系统提供了一个可以通过硬件实现的解决方案,用于以优异的性能进行联合除和拆除.
- 轻量级网络和高效的硬件平台在模型复杂性和处理速度方面提供了优势.
- 可适应的架构适合各种边缘嵌入式图像处理场景.
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