张显石, 宋健, 宋泗锦, 李永杰. 生物视觉启发的低照度视频自适应增强设计与FPGA加速实现[J]. 电子与信息学报, 2023, 45(8): 2739-2748. doi: 10.11999/JEIT221346 引用本文: 张显石, 宋健, 宋泗锦, 李永杰. 生物视觉启发的低照度视频自适应增强设计与FPGA加速实现[J]. 电子与信息学报, 2023, 45(8): 2739-2748. doi: 10.11999/JEIT221346 ZHANG Xianshi, SONG Jian, SONG Sijin, LI Yongjie. Design of Biological-inspired Low-light Video Adaptive Enhancement and FPGA Accelerated Implementation[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2739-2748. doi: 10.11999/JEIT221346 Citation: ZHANG Xianshi, SONG Jian, SONG Sijin, LI Yongjie. Design of Biological-inspired Low-light Video Adaptive Enhancement and FPGA Accelerated Implementation[J]. Journal of Electronics & Information Technology , 2023, 45(8): 2739-2748. doi: 10.11999/JEIT221346 张显石, 宋健, 宋泗锦, 李永杰. 生物视觉启发的低照度视频自适应增强设计与FPGA加速实现[J]. 电子与信息学报, 2023, 45(8): 2739-2748. doi: 10.11999/JEIT221346 引用本文: 张显石, 宋健, 宋泗锦, 李永杰. 生物视觉启发的低照度视频自适应增强设计与FPGA加速实现[J]. 电子与信息学报, 2023, 45(8): 2739-2748. doi: 10.11999/JEIT221346 ZHANG Xianshi, SONG Jian, SONG Sijin, LI Yongjie. Design of Biological-inspired Low-light Video Adaptive Enhancement and FPGA Accelerated Implementation[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2739-2748. doi: 10.11999/JEIT221346 Citation: ZHANG Xianshi, SONG Jian, SONG Sijin, LI Yongjie. Design of Biological-inspired Low-light Video Adaptive Enhancement and FPGA Accelerated Implementation[J]. Journal of Electronics & Information Technology , 2023, 45(8): 2739-2748. doi: 10.11999/JEIT221346 作者简介:

张显石：男，博士，助理研究员，研究方向为视觉认知计算、计算机视觉与类脑智能

宋健：男，硕士，研究方向为大脑视觉系统的信息处理机制、计算机建模及其在计算机视觉(图像/视频处理)中的应用

宋泗锦：男，硕士生，研究方向为基于FPGA的实时图像处理

李永杰：男，博士，教授，研究方向为大脑视觉系统的信息处理机制、计算机建模及其在计算机视觉(图像/视频处理)中的应用、智能医学图像处理

通讯作者: 李永杰 liyj@uestc.edu.cn

中图分类号: TN911.73

该文基于现场可编程门阵列实现了受生物视觉机制启发的夜间图像增强模型，实时高效地对夜间低照度视频图像进行自适应增强。受初级视觉系统中大小细胞通路启发，该文采取独立的两条通路分别处理结构与细节信息，获得了较好的处理效果与处理效率。为了实现对高清视频的实时增强，基于现场可编程门阵列对该文算法进行了加速实现。通过滑动数据窗并行处理、相邻帧信息共享、多通道并行化等硬件设计保证高数据吞吐量。该设计在 XC7Z100现场可编程门阵列上达到对1080P@60 Hz彩色视频增强的实时性要求。与本领域已有设计相比，该文设计具有更高的数据吞吐量，适用于高分辨率实时图像增强应用。 生物视觉计算模型 /  图像增强 /  现场可编程门阵列 Abstract: A nighttime image enhancement model is proposed in this paper, which is inspired by biological vision mechanism and implemented on Field Programmable Gate Arrays (FPGA) for real-time enhancement of low-light videos and images. Inspired by the Midget cells and the Parasol cells in the early visual system, the proposed method processes the structure and detail information through two independent pathways respectively, and obtains a nice effect and efficiency. To achieve real-time enhancement of high-resolution videos, this paper implements the proposed method on Field Programmable Gate Arrays. High data throughput is ensured through hardware design such as sliding data window parallel processing, adjacent frame information sharing, and multi-channel parallelization. Implemented on Field Programmable Gate Arrays XC7Z100, the proposed design achieves processing 60 frames per second for 1024 × 768 RGB images. Compared with existing designs in this field, the proposed design has higher data throughput and is suitable for high-resolution real-time image enhancement applications. Key words: Biological vision computation model /  Image enhancement /  Field Programmable Gate Arrays (FPGA)  陈勇, 陈东, 刘焕淋, 等. 基于深度卷积神经网络的无参考低照度图像增强[J]. 电子与信息学报, 2022, 44(6): 2166–2174. doi: 10.11999/JEIT210386

CHEN Yong, CHEN Dong, LIU Huanlin, et al . Unreferenced low-lighting image enhancement based on deep convolutional neural network[J]. Journal of Electronics & Information Technology , 2022, 44(6): 2166–2174. doi: 10.11999/JEIT210386 VELUCHAMY M, BHANDARI A K, and SUBRAMANI B. Optimized bezier curve based intensity mapping scheme for low light image enhancement[J]. IEEE Transactions on Emerging Topics in Computational Intelligence , 2022, 6(3): 602–612. doi: 10.1109/TETCI.2021.3053253 KIM W. Low-light image enhancement: a comparative review and prospects[J]. IEEE Access , 2022, 10: 84535–84557. doi: 10.1109/ACCESS.2022.3197629 YANG Kaifu, ZHANG Xianshi, and LI Yongjie. A biological vision inspired framework for image enhancement in poor visibility conditions[J]. IEEE Transactions on Image Processing , 2020, 29: 1493–1506. doi: 10.1109/TIP.2019.2938310 向森, 王应锋, 邓慧萍, 等. 基于双重迭代的零样本低照度图像增强[J]. 电子与信息学报, 2022, 44(10): 3379–3388. doi: 10.11999/JEIT211593

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