Remote Sensing Single-Image Super-Resolution Using Convolutional Block Attention Residual Network With Joint Adversarial Mechanisms
As super-resolution techniques continue to evolve, there is a growing requirement for more advanced methods to capture finer details, particularly when dealing with the smaller pixels within an image. In remote sensing, enhanced spatial details can find utility in diverse applications, such as disas...
Main Authors: | Allen Patnaik, Narendra Chaudhary, M. K. Bhuyan, Sultan Alfarhood, Mejdl Safran |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10497594/ |
Similar Items
-
Remote Sensing Image Super-Resolution Adversarial Network Based on Reverse Feature Fusion and Residual Feature Dilation
by: Rui Han, et al.
Published: (2023-01-01) -
Super-Resolution Reconstruction of Terahertz Images Based on Residual Generative Adversarial Network with Enhanced Attention
by: Zhongwei Hou, et al.
Published: (2023-03-01) -
Super-Resolution Generative Adversarial Network Based on the Dual Dimension Attention Mechanism for Biometric Image Super-Resolution
by: Chi-En Huang, et al.
Published: (2021-11-01) -
Spatial Transformer Generative Adversarial Network for Robust Image Super-Resolution
by: Hossam M. Kasem, et al.
Published: (2019-01-01) -
Multiscale Attention Fusion for Depth Map Super-Resolution Generative Adversarial Networks
by: Dan Xu, et al.
Published: (2023-05-01)