Semantic-Driven Ghosting-Free Image Fusion Using Dual Gain Video Stream With FPGA

Yang Xu, Longhua Xie, Hongchuan Huang, Feihong Yu and Tingyu Zhao
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Yang Xu: Zhejiang Sci-Tech University, China
Longhua Xie: Zhejiang Sci-Tech University, China
Hongchuan Huang: Zhejiang Sci-Tech University, China
Feihong Yu: Zhejiang University, China
Tingyu Zhao: Zhejiang Sci-Tech University, China

International Journal on Semantic Web and Information Systems (IJSWIS), 2025, vol. 21, issue 1, 1-21

Abstract: Conventional methods that merge multiple images with different exposure levels often suffer from blur and ghosting due to object movement. Existing ghosting removal algorithms are usually complex and slow, making them unsuitable for real-time video applications. To address this challenge, this study proposes a novel ghosting-free image fusion method using a dual gain video stream setup on an FPGA. IMX662 image sensor is employed, which simultaneously captures both HCG and LCG images with the same exposure time, enabling efficient HDR image synthesis. The proposed method directly addresses the source of the problem, eliminating the need for post-processing steps, thereby preserving algorithmic simplicity. Experimental results reveal that the proposed method not only removes ghosting by 100% but also processes data on an FPGA 98.79% faster than traditional software-based HDR fusion techniques, enabling real-time video stream processing. This dual gain, ghosting-free fusion algorithm demonstrates promising potential for use in high-speed photography and surveillance.

Date: 2025
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