Pengwei Liang

TL;DR: This paper proposes a novel method to fuse two types of information using a generative adversarial network, termed as FusionGAN, which establishes an adversarial game between a generator and a discriminator, where the generator aims to generate a fused image with major infrared intensities together with additional visible gradients.

TL;DR: This work proposes a novel unsupervised framework for pan-sharpening based on a generative adversarial network, termed as Pan-GAN, which does not rely on the so-called ground-truth during network training and has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.

TL;DR: This paper proposes an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning that is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods.

TL;DR: 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.

TL;DR: A deep transfer learning method for prior knowledge embedding and mixed layer for better feature extraction is proposed, which exhibits a large improvement in military object recognition under small training set.