  Background: Leaning redundant and complementary relationships is a critical
step in the human visual system. Inspired by the infrared cognition ability of
crotalinae animals, we design a joint convolution auto-encoder (JCAE) network
for infrared and visible image fusion. Methods: Our key insight is to feed
infrared and visible pair images into the network simultaneously and separate
an encoder stream into two private branches and one common branch, the private
branch works for complementary features learning and the common branch does for
redundant features learning. We also build two fusion rules to integrate
redundant and complementary features into their fused feature which are then
fed into the decoder layer to produce the final fused image. We detail the
structure, fusion rule and explain its multi-task loss function. Results: Our
JCAE network achieves good results in terms of both subjective effect and
objective evaluation metrics.
