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We propose an unsupervised image fusion architecture for multiple applicationscenarios based on the combination of multi-scale discrete wavelet transformthrough regional energy and deep learning. To our best knowledge, this is thefirst time the conventional image fusion method has been combined with deeplearning. The useful information of feature maps can be utilized adequatelythrough multi-scale discrete wavelet transform in our proposed method.Comparedwith other state-of-the-art fusion method, the proposed algorithm exhibitsbetter fusion performance in both subjective and objective evaluation.Moreover, it's worth mentioning that comparable fusion performance trained inCOCO dataset can be obtained by training with a much smaller dataset with onlyhundreds of images chosen randomly from COCO. Hence, the training time isshortened substantially, leading to the improvement of the model's performanceboth in practicality and training efficiency.

WaveFuse: A Unified Deep Framework for Image Fusion with Discrete Wavelet Transform