基于深度学习的多曝光图像融合(Multi-exposure Image Fusion)论文及代码整理
首先附上近期整理基于深度学习的图像融合论文的思维导图
本篇博客主要整理基于深度学习的多曝光图像融合的论文和代码
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基于卷积神经网络的图像融合框架
1. DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs [DeepFuse(ICCV 2017)] [Paper] [Code]
2. Multi-exposure fusion with CNN features [CNN(ICIP 2018)] [Paper] [Code]
3. Deep guided learning for fast multi-exposure image fusion [DeepFuse(MEF-Net(TIP 2020))] [Paper] [Code]
4. Multi-exposure high dynamic range imaging with informative content enhanced network [ICEN(NC 2020)] [Paper]
5. Deep coupled feedback network for joint exposure fusion and image super-resolutions [CF-Net(TIP 2021)] [Paper] [Code]
6. Deep unsupervised learning based on color un-referenced loss functions for multi-exposure image fusion [UMEF(IF 2021)] [Paper] [Code]
7. Automatic Intermediate Generation With Deep Reinforcement Learning for Robust Two-Exposure Image Fusion [DRLF(TNNLS 2021)] [Paper]
8. DMulti-exposure image fusion via deep perceptual enhancement [DPE-MEF(IF 2022)] [Paper] [Code]
9. TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning [TransMEF(AAAI 2022)] [Paper] [Code]
基于生成对抗网络的图像融合框架
1. MEF-GAN: Multi-Exposure Image Fusion via Generative Adversarial Networks [MEF-GAN(TIP 2020)] [Paper] [Code]
2. Two exposure fusion using prior-aware generative adversarial network [PA-GAN(TMM 2021)] [Paper]
3. Attention-guided Global-local Adversarial Learning for Detail-preserving Multi-exposure Image Fusion [AGAL(TCSVT 2021)] [Paper] [Code]
4. GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks [GANFuse(NCAA 2021)] [Paper]
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