Single-frame super-resolution for remote sensing images based on improved deep recursive residual network

Author:

Tang Jiali,Zhang Jie,Chen Dan,Al-Nabhan Najla,Huang Chenrong

Abstract

AbstractSingle-frame image super-resolution (SISR) technology in remote sensing is improving fast from a performance point of view. Deep learning methods have been widely used in SISR to improve the details of rebuilt images and speed up network training. However, these supervised techniques usually tend to overfit quickly due to the models’ complexity and the lack of training data. In this paper, an Improved Deep Recursive Residual Network (IDRRN) super-resolution model is proposed to decrease the difficulty of network training. The deep recursive structure is configured to control the model parameter number while increasing the network depth. At the same time, the short-path recursive connections are used to alleviate the gradient disappearance and enhance the feature propagation. Comprehensive experiments show that IDRRN has a better improvement in both quantitation and visual perception.

Funder

Jiangsu Postdoctoral Research Foundation

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Information Systems,Signal Processing

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Restoration of Semantic-Based Super-Resolution Aerial Images;Informatics and Automation;2024-06-26

2. WerfNet: wide effective receptive field network for super-resolution of remote sensing images;2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC);2024-05-24

3. IMU-CNN: implementing remote sensing image restoration framework based on Mask-Upgraded Cascade R-CNN and deep autoencoder;Multimedia Tools and Applications;2024-01-30

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