Analysis of single image super resolution models
Yükleniyor...
Tarih
2022-11-18
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Image Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. This article aims and seeks to provide a comprehensive analysis on recent advances of models which has been used in image superresolution. This study has been investigated over other essential topics of current model problems, such as publicly accessible benchmark data-sets and performance evaluation measures. Finally, The study concluded these analysis by highlighting several weaknesses of existing base models as their feeding strategy and approved that the training technique which is Blind Feeding, which led several model to achieve state-of-the art.
Açıklama
Anahtar Kelimeler
Convolutional neural network, Generative adversarial networks, Image processing, Single image super resolution, Benchmarking, Convolutional neural networks, Deep learning, Image analysis, Image enhancement, Optical resolving power, Comprehensive analysis, Current modeling, Image processing technique, Image super resolutions, Images processing, Learning approach, Single images, Super-resolution models
Kaynak
2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Köprülü, M. & Eskil. M. T. (2022). Analysis of single image super resolution models. Paper presented at the 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 1-6. doi:10.1109/ICECCME55909.2022.9988599
