# Dense-FG: A Fusion GAN Model by Using Densely Connected Blocks to Fuse Infrared and Visible Images  
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2 College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China;  
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of target objects, thereby improving the accuracy of navigation and monitoring. In the realm of firefighting and rescue operations, infrared imaging can identify fire origins and smoke, while visible imaging can offer clearer representations of building architecture [4]. By combining the two, rescue personnel can pinpoint fire sources and smoke more swiftly and precisely, thus enhancing rescue efficiency. Image fusion technology has also been extensively employed in the medical domain. By providing enhanced medical images with improved clarity and comprehensiveness, it ultimately leads to a higher degree of accuracy and efficiency in medical diagnoses. In addition, infrared and visible image fusion technology is extensively utilized in information security, natural resource management, and environmental monitoring [5]. In summary, the technology has broad application potential, providing more comprehensive and accurate information for many fields. Research into the technology of fusing infrared and visible images holds significant practical importance and is valuable for many applications. Currently, there are mainly two methods for fusing infrared and visible images: traditional image transformation decomposition and synthesis methods, and deep learning methods [6]. Traditional methods have low detail presentation and significant differences in fusion results under different scenarios, thus their applicability is limited. Deep learning methods mainly use CNN, GAN, DenseNet, and other methods to construct networks for fusion [7; 8; 9], which can cover more usage scenarios, but the balance effect between infrared target brightness and visible texture details is not good. Image texture details are unclear and infrared target details are lost, requiring further improvement in fusion image quality. This article proposes a GAN network fusion method using dense connection blocks for infrared and visible image fusion, which fully utilizes the source image information while preserving more complementary information. The texture details are more abundant and clearer, and the important features of the image pairs are retained, resulting in higher fusion quality and better visual effects. To summarize, the main contributions of our research can be shown as follows:  
1. A generator network structure and discriminator network structure with dense connection blocks were designed so that there are paths connecting all layers of the network, enabling feature reuse and improving computational efficiency. 2. A content loss function was constructed using four losses, an infrared gradient, visible intensity, infrared intensity, and a visible gradient, to maintain a balance between infrared radiation information and visible texture details, and to achieve an ideal fusion image. 3. By updating the 8-direction gradient operator template and optimizing the design of the loss function, the fusion image details were made richer. 4. Histogram comparison was used to demonstrate the fusion ability of eight fusion methods in a clear and intuitive way, providing a reference for the development and improvement of fusion image methods in the future. The remaining sections of this article are organized as follows. Section 2 introduces some related work on image fusion from both traditional and deep learning methods.