Double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion

Abstract

At present, visible light imaging sensor and infrared imaging sensor are two commonly used sensors, which are widely used in aviation, navigation and other military fields of detection, monitoring and tracking. Due to their different working principles, their performance is different. The infrared imaging sensor records the infrared radiation information of the target itself by acquiring the infrared radiation of the ground target. It identifies the target by detecting the thermal radiation difference between the target and the background, so it has special recognition and camouflage ability, such as finding people, vehicles and artillery hidden in the woods and grass. Although the infrared imaging sensor has a good detection performance for thermal targets, it is insensitive to the brightness changes of the scene and has low imaging resolution, which is not conducive to human eyes interpretation. Visible light imaging sensor is sensitive to the reflection of the target scene and has nothing to do with the thermal contrast of the target scene. The obtained image has high clarity and can provide the details of the target scene. Therefore, the fusion of infrared and visible images will be beneficial to the combination of infrared image's better target indication characteristics and visible image's scene clearing information. In this paper, we propose a double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion. The experimental results show that the fusion image not only retains the target information of the infrared image, but also retains more details of the visible image, and achieves better performance in both subjective and objective evaluation