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This work is based on superresolution generative adversarial nets (SRGANs), where we modify the loss function and the structure of the network of SRGANs and propose the improved SRGAN (ISRGAN), which makes ... Considering the tradeoff between high spatial and high temporal resolution in remote sensing images, many learningbased models (e.g., Convolutional neural network, sparse coding, Bayesian network) have ... To overcome this shortage, a super-resolution generative adversarial network model (SRGAN) was presented by replacing the original CNN structure with the generative adversarial network (GAN)  . ...doi:10.3390/rs12081263 fatcat:3cey4ixtubb6zblkkbnv5uaesy
International Journal of Performability Engineering
The super-resolution reconstruction algorithm based on generative adversarial network (GAN) can generate realistic texture in the superresolution process of a single remote sensing image. ... In order to further improve the visual quality of the reconstructed image, this paper will improve the generation network, discrimination network, and perceptual loss of the generated confrontation network ... Therefore, this paper uses the Super-Resolution (SR) reconstruction algorithm to improve the resolution of remote sensing images. ...doi:10.23940/ijpe.19.07.p4.17831791 fatcat:fml4raubgzejjf537pbfnaakae
INDEX TERMS Generative adversarial network, super-resolution imaging, image reconstruction, total variation, loss function. ... is designed based on the adaptive diagonal total-variation generative adversarial network. ... generative adversarial network. ...doi:10.1109/access.2020.2981726 fatcat:a42bnmyxkbdanpcgjhahxadssi