Mars Image Super-Resolution Based on Generative Adversarial Network
High-resolution (HR) Mars images have great significance for studying the land-form features of Mars and analyzing the climate on Mars. Nowadays, the mainstream image super-resolution methods are based on deep learning or CNNs, which are better than traditional methods. However, these deep learning...
Main Authors: | Cong Wang, Yin Zhang, Yongqiang Zhang, Rui Tian, Mingli Ding |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9503382/ |
Similar Items
-
Spatial Transformer Generative Adversarial Network for Robust Image Super-Resolution
by: Hossam M. Kasem, et al.
Published: (2019-01-01) -
Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution
by: Youngsoo Kim, et al.
Published: (2022-01-01) -
Kernel Estimation Using Total Variation Guided GAN for Image Super-Resolution
by: Jongeun Park, et al.
Published: (2023-04-01) -
Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
by: Qiang Wang, et al.
Published: (2022-06-01) -
Video Super-Resolution Based on Generative Adversarial Network and Edge Enhancement
by: Jialu Wang, et al.
Published: (2021-02-01)