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A Multi-scale Information Integration Framework for Infrared and Visible Image Fusion by Yang, Guang; Li, Jie; Lei, Hanxiao; Gao, Xinbo is a scholarly article available to read on EtoBox.

What is A Multi-scale Information Integration Framework for Infrared and Visible Image Fusion about?

Infrared and visible image fusion aims at generating a fused image containing the intensity and detail information of source images, and the key issue is effectively measuring and integrating the complementary information of multi-modality images from the same scene. Existing methods mostly adopt a simple weight in the loss function to decide the information retention of each modality rather than adaptively measuring complementary information for different image pairs. In this study, we propose a multi-scale dual attention (MDA) framework for infrared and visible image fusion, which is designed to measure and integrate complementary information in both structure and loss function at the image and patch level. In our method, the residual downsample block decomposes source images into three scales first. Then, dual attention fusion block integrates complementary information and generates a spatial and channel attention map at each scale for feature fusion. Finally, the output image is reconstructed by the residual reconstruction block. Loss function consists of image-level, feature-level and patch-level three parts, of which the calculation of the image-level and patch-level two part

Author

Yang, Guang; Li, Jie; Lei, Hanxiao; Gao, Xinbo

Published

2023

Language

EN