融合边缘检测的遥感图像超分辨率重建算法

针对基于生成对抗网络的遥感图像超分辨率重建存在训练不稳定，参数冗余，图片纹理细节不够清晰等问题。提出一种融合边缘检测的遥感图像超分辨率重建算法。首先，在生成器网络中引入改进后的Canny边缘检测算子用于低分辨率图像特征提取，通过在Canny算子边缘提取流程中利用双边滤波和3×3邻域梯度以检测图像的边缘信息，使网络能够更好的表达高频特征；其次，为降低网络参数和提高网络训练的稳定性，去除判别器网络中冗余的BN层，同时将Wasserstein距离定义为对抗损失以解决生成对抗网络训练出现的梯度消失现象。在NWPU RESISC45数据集上，所提方法的峰值信噪比与结构相似性较WDSR和CARN算法分别提升了1.22 dB、0.114和0.32 dB、0.013,且重建后的图像相比较WDSR、CARN等其他SR算法在图像纹理细节和主观视觉效果方面也均有提升。

Super-resolution reconstruction of remote sensing image based on edge detection

Remote sensing image super resolution reconstruction based on Generative adversarial networks has some problems, such as unstable training, redundant parameters and unclear texture details. This paper presents a super resolution reconstruction algorithm of remote sensing image based on edge detection. Firstly, the improved Canny edge detection operator is introduced into the generator network for low resolution image feature extraction. Bilateral filtering and 3×3 neighborhood gradient are used to detect image edge information in the Canny operator edge extraction process, so that the network can better express high frequency features. Secondly, in order to reduce the network parameters and improve the stability of network training, the redundant BN layer in the discriminator network is removed, and the Wasserstein distance is defined as adversarial loss to solve the gradient disappearance phenomenon in generating adversarial network training. On the NWPU RESISC45 dataset, Compared with WDSR and CARN, the peak signal to noise ratio and structural similarity of the proposed method are improved by 1.22 dB,0.114 and 0.32 dB,0.013, respectively. Moreover, compared with other SR algorithms such as WDSR and CARN, the reconstructed images are improved in texture details and subjective visual effects.