Research on the Camouflage Characteristics of a Microfluidic Vision Camouflage System Based on the Image Inpainting Algorithm

Abstract

Combining deep-learning image inpainting algorithms with the microfluidic technology, the paper proposes a method to achieve dynamic stealth and camouflage by using a microfluidic vision camouflage system simulating the chameleon skin. The basic principle is to perceive color changes in the external environment and collect ambient image information, and then utilize the image inpainting algorithm to adjust the control signals of the microfluidic system in real time. The detailed working principle of the microfluidic vision camouflage system is presented, and the mechanism of generating control signals for the system through deep-learning image inpainting algorithms and image-processing techniques is elucidated. The camouflage effect of the chameleon skin is analyzed and evaluated using color similarity. Results indicate that the camouflaged images are consistent with the background environment, thereby improving the target’s stealth and maneuvering characteristics. The camouflage technology developed in the paper based on the microfluidic vision camouflage system can be applied to several situations, such as military camouflage uniforms, robot skins, and weapon equipment.