# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 6 Future Prospects
These two techniques can produce high-dynamic-range images from a small number of input photographs, but they are unable to provide additional image enhancement. There is still an issue to be solved on how to maintain the balance of detailed information and contrast in the fusion process while maintaining the dynamic range of the two images. 3. Real-time image fusion Real-time performance is a crucial need in various application scenarios, including surveillance, night vision, autonomous vehicles, and the military and security sectors. The development of infrared and visible image fusion in real-world applications will be hampered by the existing infrared and visible image fusion algorithms' potential performance limitations when processing large-scale images, which takes a lot of time and computational resources. [PERSON] et al. attempted to create a deeper learning-based approach to infrared and visible picture fusion in literature [112]; however, there is currently very little study in this area. The challenge remains in figuring out how to further reduce computer complexity, lower memory, and processor needs while maintaining good fusion outcomes and accomplishing high-quality real-time fusion with constrained computing resources. 4. Adaptability to specific scenarios For infrared and visible picture fusion, different application scenarios have varied conditions and requirements. There is currently no general algorithm that can be used in all situations. It is occasionally required to undertake research for a particular environment in order to suit the needs of unique application scenarios. The majority of the visible and infrared image fusion techniques now in use do not consider their practical use. Many redundant or even erroneous pieces of information are added during the fusion process as a result of some end-to-end models' failure to discriminate between different areas of various source images while building loss functions, which weakens the valuable information in the fusion image. For instance, the literature in [25] provides a typical example. U2 Fusion is a typical method based on CNN, and FusionGAN is a typical method based on GAN. When these two approaches are used for object detection, it turns out that FusionGAN decreases the background texture, whereas U2 Fusion weakens the prominent object. Both approaches fail to fully exploit their advantages. In order to achieve the detection of salient targets as well as the extraction and reconstruction of useful information, [PERSON] et al. presented the STDFusionNet technique. Rich textures can still be seen while highlighting significant targets in the merged photos. The techniques for fusing infrared and visible images are currently becoming more and more varied, and the complexity of the scenes to be handled is rising. It is a very challenging task to develop an algorithm that can adapt to various scenarios and perform well on various indicators. As a result, we must commit to conducting additional research in order to develop more flexible and reliable picture fusion techniques and offer the best solutions for certain scenes. 5. Standard, comprehensive integration evaluation indicators There is no established fusion evaluation index in the field of image fusion, and the fusion findings frequently perform well for some indications but poorly for others [113]. As a result, it is impossible to evaluate the performance of the picture fusion method accurately and properly. Therefore, in order to evaluate the image fusion algorithm thoroughly, it is important to offer a fairly full fusion evaluation index. 6. Converter-based infrared and visible image fusion method Converter-based techniques for performing infrared and visible picture fusion were introduced in the field of image fusion in 2021. The converter in this method converts the visible and infrared images into a particular feature space, and the features into two features.