Research on Infrared and Visible Light Image Fusion Algorithm Based on Deep Neural Networks
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.
image fusiondeep neural networksperceived lossadversarial training
万方数据
维普
