LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images

Deep learning based fusion methods have been achieving promising performance in image fusion tasks. This is attributed to the network architecture that plays a very important role in the fusion process. However, in general, it is hard to specify a good fusion architecture, and consequently, the design of fusion networks is still a black art, rather than science. To address this problem, we formulate the fusion task mathematically, and establish a connection between its optimal solution and the network architecture that can implement it. This approach leads to a novel method proposed in the paper of constructing a lightweight fusion network. It avoids the time-consuming empirical network design by a…

• NN
  National Natural Science Foundation of China

  Awards: U1836218, 62202205, 62020106012, 62106089
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/R018456/1, EP/N007743/1, EP/N007743/1, EP/R018456/1
• FR
  Fundamental Research Funds for the Central Universities

  Award: JUSRP123030