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

Source: [WaveFuse: A Unified Deep Framework for Image Fusion with Discrete Wavelet Transform](<https://www.sota2.com/research/paper/68fa58881e209131ce9af0c4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:55:58.761Z

Authors: Shaolei Liu, Manning Wang, Zhijian Song

Published: 2020-07-28T10:30:47.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.14110>)

[PDF](<https://arxiv.org/pdf/2007.14110.pdf>)