FPGA-Based Hardware Acceleration Technology and Its Application in Image Processing

DOI

10.2991/978-94-6463-821-9_41How to use a DOI?

Keywords

Field Programmable Gate Arrays; Image Processing; Hardware Design Languages

Abstract

With the expansion of image processing application scenarios and the diversification of image processing demands across different fields, Field-Programmable Gate Array (FPGA)-based hardware acceleration technology has become an effective solution to overcome the limitations of traditional software implementations. This paper reviews the latest research progress on FPGA applications in image processing, covering multiple application areas such as convolution computing, real-time medical image processing, machine vision inspection, microarray image analysis, and robotic welding. By leveraging parallel computing, pipelining optimization, and hardware acceleration strategies, FPGA-based implementations achieve an optimal balance between processing efficiency, power consumption, and computational performance. This enables FPGA to be widely applied across different domains and makes it the optimal solution for specific application scenarios. Research shows that FPGA architectures can effectively accelerate convolution operations and enable high-speed multi-channel image acquisition and processing, which ultimately enhances medical image quality. Additionally, integrating FPGA into heterogeneous computing environments and System-on-Chip (SoC) designs enhances system scalability and adaptability, meeting diverse application needs. This paper highlights the critical role of FPGA technology in real-time, efficient, and low-power image processing applications and provides in-depth insights into its future applications and challenges.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xuefei Shi
PY  - 2025
DA  - 2025/08/31
TI  - FPGA-Based Hardware Acceleration Technology and Its Application in Image Processing
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 394
EP  - 404
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_41
DO  - 10.2991/978-94-6463-821-9_41
ID  - Shi2025
ER  -