光学与光电技术, 2019, 17 (6): 39, 网络出版: 2020-01-07
基于生成对抗网络的遥感图像超分辨率重建
Remote Sensing Image Superresolution Reconstruction Based on GAN
生成对抗网络 遥感图像超分辨率重建 残差密集块 迁移学习 感知损失 generative adversarial network remote sensing image superresolution reconstructio residual dense block transfer learning perceptual loss
摘要
生成对抗网络模型可以用来生成服从原始真实图像分布规律的高频细节信息。为了进一步提高重建图像的视觉质量,对生成对抗网络的生成网络、判别网络及感知损失三个方面进行了改进。首先移除了生成网络中的BN 层,同时在残差块中采用密集连接的方式,增加网络模型的容量,降低了计算复杂性,增强了网络训练的稳定性。然后采用迁移学习技术来促进深度模型的训练,解决了遥感数据不足的问题。实验结果表明提出的算法通过对遥感图像超分辨率重建算法进行改进,可以获得更好的主观视觉效果,PSNR 和SSIM 均有显著提高。
Abstract
Generative adversarial network model can be used to generate high-frequency detail information which obeys the distribution of original real image. In order to further improve the visual quality of the reconstructed image,the generation network,discrimination network and perceptual loss of the generated confrontation network are improved in this paper. Firstly,the BN layer in the generated network is removed. Meanwhile,dense connections are used in the residual blocks to increase the capacity of the network model,reduce the computational complexity and enhance the stability of network training. Then the transfer learning technology is used to promote the training of depth model,which solves the problem of insufficient remote sensing data. The experimental results show that the proposed algorithm improves the super-resolution reconstruction algorithm of remote sensing images and can obtain better subjective visual effects. PSNR and SSIM are improved visibly.
李昂, 宋晓莹. 基于生成对抗网络的遥感图像超分辨率重建[J]. 光学与光电技术, 2019, 17(6): 39. LI Ang, SONG Xiao-ying. Remote Sensing Image Superresolution Reconstruction Based on GAN[J]. OPTICS & OPTOELECTRONIC TECHNOLOGY, 2019, 17(6): 39.