Infrared and Visible Image Fusion via Decoupling Network

In general, the goal of existing infrared and visible image fusion (IVIF) methods is to make the fused image contain both the high-contrast regions of the infrared image and the texture details of the visible image. Also, a hybrid loss function constructed with weight fidelity loss, gradient loss, and decoupling loss which ensures the fusion image to be generated to effectively preserves the source image’s texture details and luminance information.

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• 1 However, this definition would lead the fusion image losing information from the visible image in high-contrast areas.
• 2 For this problem, this paper proposed a decoupling network-based IVIF method (DNFusion), which utilizes the decoupled maps to design additional constraints on the network to force the network to retain the saliency information of the source image effectively.
• 3 The current definition of image fusion is satisfied while effectively maintaining the saliency objective of the source images.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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