GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion

Visible images contain rich texture information, whereas infrared images have significant contrast. It is advantageous to combine these two kinds of information into a single image so that it not only has good contrast but also contains rich texture details. In general, previous fusion methods cannot achieve this goal well, where the fused results are inclined to either a visible or an infrared image. To address this challenge, a new fusion framework called generative adversarial network with multiclassification constraints (GANMcC) is proposed, which transforms image fusion into a multidistribution simultaneous estimation problem to fuse infrared and visible images in a more reasonable way. We adopt a…

• NN
  National Natural Science Foundation of China

  Awards: 61773295, 41890820
• NS
  Natural Science Foundation of Hubei Province

  Award: 2019CFA037