  Single Image Super-resolution (SISR) produces high-resolution images with
fine spatial resolutions from aremotely sensed image with low spatial
resolution. Recently, deep learning and generative adversarial networks(GANs)
have made breakthroughs for the challenging task of single image
super-resolution (SISR). However, thegenerated image still suffers from
undesirable artifacts such as, the absence of texture-feature representationand
high-frequency information. We propose a frequency domain-based spatio-temporal
remote sensingsingle image super-resolution technique to reconstruct the HR
image combined with generative adversarialnetworks (GANs) on various frequency
bands (TWIST-GAN). We have introduced a new method incorporatingWavelet
Transform (WT) characteristics and transferred generative adversarial network.
The LR image hasbeen split into various frequency bands by using the WT,
whereas, the transfer generative adversarial networkpredicts high-frequency
components via a proposed architecture. Finally, the inverse transfer of
waveletsproduces a reconstructed image with super-resolution. The model is
first trained on an external DIV2 Kdataset and validated with the UC Merceed
Landsat remote sensing dataset and Set14 with each image sizeof 256x256.
Following that, transferred GANs are used to process spatio-temporal remote
sensing images inorder to minimize computation cost differences and improve
texture information. The findings are comparedqualitatively and qualitatively
with the current state-of-art approaches. In addition, we saved about 43% of
theGPU memory during training and accelerated the execution of our simplified
version by eliminating batchnormalization layers.
