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Intell. Syst. Technol."],"published-print":{"date-parts":[[2021,12,31]]},"abstract":"<jats:p>\n            <jats:bold>Single Image Super-resolution (SISR)<\/jats:bold>\n            produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and\n            <jats:bold>generative adversarial networks (GANs)<\/jats:bold>\n            have made breakthroughs for the challenging task of\n            <jats:bold>single image super-resolution (SISR)<\/jats:bold>\n            . However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating\n            <jats:bold>Wavelet Transform (WT)<\/jats:bold>\n            characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256\u00a0\u00d7\u00a0256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers.\n          <\/jats:p>","DOI":"10.1145\/3456726","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T17:29:58Z","timestamp":1640021398000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution"],"prefix":"10.1145","volume":"12","author":[{"given":"Fayaz Ali","family":"Dharejo","sequence":"first","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1987-2402","authenticated-orcid":false,"given":"Farah","family":"Deeba","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2144-1131","authenticated-orcid":false,"given":"Yuanchun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhagwan","family":"Das","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Quaid-e-Awam University Engineering Science and Technology, Nawasbshah, Sindh, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Munsif Ali","family":"Jatoi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Salim Habib University, Karachi, Sindh, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Zawish","sequence":"additional","affiliation":[{"name":"Walton Institute for Information and Communication Systems Science, Waterford, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Du","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5222-248X","authenticated-orcid":false,"given":"Xuezhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3004536"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs12030587"},{"issue":"0","key":"e_1_3_1_4_2","first-page":"0","article-title":"A plexus-convolutional neural network framework for fast remote sensing image super-resolution in wavelet domain","volume":"0","author":"Deeba Farah","year":"2021","unstructured":"Farah Deeba, Yuanchun Zhou, Fayaz Ali Dharejo, Muhammad Ashfaq Khan, Bhagwan Das, Xuezhi Wang, and Yi Du. 2021. 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