22 April 2022 Multi-exposure image fusion for dynamic scenes with ghosting removal
Zihan Xu, Jinling Chen, Maokai Cheng, Jie Li
Author Affiliations +
Abstract

Multi-exposure image fusion algorithms in dynamic scenes usually have ghosts. Most of the existing ghost-free methods have complex calculations and the details will be lost. To solve these problems, we present a multi-exposure fusion method, which can remove ghosts and preserve more details of the image. The method detects ghost images by the improved frame difference method and calculates the early weight maps, combining the ghost detection binary image and initial weight maps to redefine weight maps. Then with the improved Laplacian pyramid algorithm, a high-quality image with more details is fused quickly. This is a significant improvement over existing techniques by objective and subjective comparisons.

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Zihan Xu, Jinling Chen, Maokai Cheng, and Jie Li "Multi-exposure image fusion for dynamic scenes with ghosting removal," Journal of Electronic Imaging 31(2), 023036 (22 April 2022). https://doi.org/10.1117/1.JEI.31.2.023036
Received: 9 November 2021; Accepted: 5 April 2022; Published: 22 April 2022
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KEYWORDS
Image fusion

Lithium

Binary data

Image processing

Fusion energy

Detection and tracking algorithms

Image quality

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