Pyramid Information Distillation Attention Network for Super-Resolution Reconstruction of Remote Sensing Images

Image super-resolution (SR) technology aims to recover high-resolution images from low-resolution originals, and it is of great significance for the high-quality interpretation of remote sensing images. However, most present SR-reconstruction approaches suffer from network training difficulties and...

Full description

Saved in:
Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 13; no. 24; p. 5143
Main Authors Huang, Bo, Guo, Zhiming, Wu, Liaoni, He, Boyong, Li, Xianjiang, Lin, Yuxing
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2021
Subjects
Algorithms
attention mechanism
Complexity
Computer applications
Distillation
feature distillation
Feature extraction
Image quality
Image reconstruction
Image resolution
Modules
Neural networks
Receptive field
Remote sensing
Sensitivity enhancement
Spatial distribution
super-resolution
Online AccessGet full text

Cover

More Information
Summary:Image super-resolution (SR) technology aims to recover high-resolution images from low-resolution originals, and it is of great significance for the high-quality interpretation of remote sensing images. However, most present SR-reconstruction approaches suffer from network training difficulties and the challenge of increasing computational complexity with increasing numbers of network layers. This indicates that these approaches are not suitable for application scenarios with limited computing resources. Furthermore, the complex spatial distributions and rich details of remote sensing images increase the difficulty of their reconstruction. In this paper, we propose the pyramid information distillation attention network (PIDAN) to solve these issues. Specifically, we propose the pyramid information distillation attention block (PIDAB), which has been developed as a building block in the PIDAN. The key components of the PIDAB are the pyramid information distillation (PID) module and the hybrid attention mechanism (HAM) module. Firstly, the PID module uses feature distillation with parallel multi-receptive field convolutions to extract short- and long-path feature information, which allows the network to obtain more non-redundant image features. Then, the HAM module enhances the sensitivity of the network to high-frequency image information. Extensive validation experiments show that when compared with other advanced CNN-based approaches, the PIDAN achieves a better balance between image SR performance and model size.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13245143