This article proposes and optimizes an infrared and visible light image fusion algorithm based on deep neural networks,using two optimization strategies:perceptual loss and adversarial training.Through experimental verification,the optimization algorithm surpasses traditional methods in image quality.Perceived loss improves the clarity and semantic information of fused images,while adversarial training enhances realism and detail preservation.This study introduces advanced deep learning methods into the field of infrared and visible light image fusion,providing strong support for technical applications in related fields.
关键词
图像融合/深度神经网络/感知损失/对抗性训练
Key words
image fusion/deep neural networks/perceived loss/adversarial training
维普
万方数据