Strong ghost removal in multi-exposure image fusion using hole-filling with exposure congruency
Author:
Funder
National Natural Science Foundation of China
Publisher
Elsevier BV
Subject
Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Media Technology,Signal Processing
Reference51 articles.
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3. K.R. Prabhakar, R. Arora, A. Swaminathan, K.P. Singh, R.V. Babu. A Fast, scalable, and reliable deghosting method for extreme exposure fusion, in: IEEE International Conference on Computational Photography (ICCP), 2019, Cluj-Napoca, Romania, 5–7 Sept., pp. 1–8.
4. Ghosting-free DCT based multi-exposure image fusion;Martorell;Signal Process.-Image Commun.,2019
5. Q. Yan, D. Gong, Q. Shi, A. Hengel, C. Shen, I. Reid, Y. Zhang. Attention-guided network for ghost-free high dynamic range imaging, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, Los Angeles CA, United States, 16–20 June, pp. 1751–1760.
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Multi-Exposure Image Fusion Techniques: A Comprehensive Review;Remote Sensing;2022-02-07
2. Tensor Product and Tensor-Singular Value Decomposition based Multi-exposure Fusion of Images;IEEE Transactions on Multimedia;2021
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