论文标题
用于红外和可见图像融合的联合卷积自动编码网络
A Joint Convolution Auto-encoder Network for Infrared and Visible Image Fusion
论文作者
论文摘要
背景：倾斜冗余和互补关系是人类视觉系统的关键步骤。受Crotalinae动物的红外认知能力的启发，我们为红外和可见图像融合设计了联合卷积自动编码器（JCAE）网络。方法：我们的关键见解是同时将红外和可见的对图像馈送到网络中，并将编码器流分成两个私人分支和一个共同的分支，私人分支为互补特征学习而工作，共同分支为冗余特征学习提供了工作。我们还构建了两个融合规则，以将冗余和互补功能集成到其融合功能中，然后将其馈入解码器层以产生最终的融合图像。我们详细说明结构，融合规则，并解释其多任务损失函数。结果：我们的JCAE网络在主观效果和客观评估指标方面都取得了良好的结果。
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.
