Master GAN: Multiple Attention is all you Need: A Multiple Attention Guided Super Resolution Network for Dems

The task of transforming low-resolution remote sensing images to high-resolution has consistently presented a formidable challenge in the field. The use of Generative Adversarial Networks (GANs) has led to tremendous development in the field. In this study, a novel super resolution architecture Mult...

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Bibliographic Details
Main Authors: Mohammed, Azhan, Kashif, Mohammad, Zama, Md Haider, Ansari, Mohammed Abbas, Ali, Saquib
Format: Conference Proceeding
Language:English
Subjects:
Codes
depth elevation maps
Generative adversarial networks
image generation
multiple attention
Neural networks
super resolution
Superresolution
Training
Transformers
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Summary:The task of transforming low-resolution remote sensing images to high-resolution has consistently presented a formidable challenge in the field. The use of Generative Adversarial Networks (GANs) has led to tremendous development in the field. In this study, a novel super resolution architecture Multiple Attention Swin Transformer Enhanced Residual GAN (MASTER GAN) has been introduced, that uses multiple attention techniques in a neural network trained in an adversarial training environment. The introduced MASTER GAN acheives state-of-the-art results in super resolution tasks, when compared to existing mechanism. The paper also introduces an open source database of low resolution and counter high resolution imagery, generated using Kernel GAN. The training code has been provided at: https://github.com/sheikhazhanmohammed/MASTERGAN.git
ISSN:2153-7003
DOI:10.1109/IGARSS52108.2023.10283196