Cross Attention-guided Dense Network for Images Fusion

TL;DR

This paper introduces a novel cross-attention-guided dense network for image fusion that models cross-image correlations to improve feature extraction and fusion quality across multiple modalities.

Contribution

The paper proposes a unified unsupervised framework with a cross-attention module and dense connections for better spatial correspondence modeling in image fusion.

Findings

Outperforms state-of-the-art methods quantitatively.

Achieves superior qualitative fusion results.

Effective in multi-modal, multi-exposure, and multi-focus image fusion.

Abstract

In recent years, various applications in computer vision have achieved substantial progress based on deep learning, which has been widely used for image fusion and shown to achieve adequate performance. However, suffering from limited ability in modeling the spatial correspondence of different source images, it still remains a great challenge for existing unsupervised image fusion models to extract appropriate feature and achieves adaptive and balanced fusion. In this paper, we propose a novel cross-attention-guided image fusion network, which is a unified and unsupervised framework for multi-modal image fusion, multi-exposure image fusion, and multi-focus image fusion. Different from the existing self-attention module, our cross-attention module focus on modeling the cross-correlation between different source images. Using the proposed cross attention module as a core block, a densely connected cross attention-guided network is built to dynamically learn the spatial correspondence to derive better alignment of important details from different input images. Meanwhile, an auxiliary branch is also designed to model the long-range information, and a merging network is attached to finally reconstruct the fusion image. Extensive experiments have been carried out on publicly available datasets, and the results demonstrate that the proposed model outperforms the state-of-the-art quantitatively and qualitatively.