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13 June 2024 Detail-preserving multi-scale exposure fusion via edge-preserving smoothing pyramids
Xinli Zhu, Yasheng Zhang, Yuqing Fang, Jiao Jiao, Qiwei Fu, Pengju Li, Wanyun Li
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Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801V (2024) https://doi.org/10.1117/12.3034003
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China
Abstract
Multi-scale exposure fusion is an effective way to directly fuse low dynamic range (LDR) image with different exposures into a content-rich LDR image for high dynamic range (HDR) reconstruction. Previous researches have shown that edge-preserving smoothing can be used to improve multi-scale exposure fusion. However, multi-scale exposure fusion via edge-preserving smoothing pyramids suffers from loss of details. To address this issue, we propose a side window gradient guided image filtering (SGGIF) and use it to construct an edge-preserving smooth pyramid. First, by adding eight kernels to the gradient guided image filtering(GGIF), a SGGIF with effective edge preserving is developed. Furthermore, we select the weight map with the minimum mean as the guidance image, which can further preserve details in the brightest and darkest regions of HDR scenes. Finally, we developed a detail-preserving multi-scale exposure fusion method based on edge-preserving smooth pyramids. Experimental results indicate that our method can effectively preserve details and reduce halo artifacts. Both quantitative and qualitative analyses demonstrate the effectiveness of our proposed approach.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Xinli Zhu, Yasheng Zhang, Yuqing Fang, Jiao Jiao, Qiwei Fu, Pengju Li, and Wanyun Li "Detail-preserving multi-scale exposure fusion via edge-preserving smoothing pyramids", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801V (13 June 2024); https://doi.org/10.1117/12.3034003
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KEYWORDS
Image fusion
Windows
Image filtering
Tunable filters
Image quality
High dynamic range imaging
Image restoration
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