论文标题
WaveFuse：一个统一的深层框架，用于与离散小波变换的图像融合
WaveFuse: A Unified Deep Framework for Image Fusion with Discrete Wavelet Transform
论文作者
论文摘要
我们根据多尺度离散小波通过区域能量和深度学习的组合，为多种应用程序场景提出了一个无监督的图像融合体系结构。据我们所知，这是传统图像融合方法与深度学习结合的第一次。特征地图的有用信息可以通过我们所提出的方法中的多尺度离散小波变换来充分利用，并使用其他最先进的融合方法，而所提出的算法在主观和客观评估中都表现出更好的融合性能。此外，值得一提的是，可以通过使用较小的数据集进行训练，只有数百张图像从可可中随机选择，可以通过训练可可数据集进行训练。因此，训练时间大大缩短，从而改善了模型在实用性和培训效率方面的表现。
We propose an unsupervised image fusion architecture for multiple application scenarios based on the combination of multi-scale discrete wavelet transform through regional energy and deep learning. To our best knowledge, this is the first time the conventional image fusion method has been combined with deep learning. The useful information of feature maps can be utilized adequately through multi-scale discrete wavelet transform in our proposed method.Compared with other state-of-the-art fusion method, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation. Moreover, it's worth mentioning that comparable fusion performance trained in COCO dataset can be obtained by training with a much smaller dataset with only hundreds of images chosen randomly from COCO. Hence, the training time is shortened substantially, leading to the improvement of the model's performance both in practicality and training efficiency.
