Next Article in Journal
Acoustic Sensors for Air and Surface Navigation Applications
Previous Article in Journal
An Improved Calibration Method for a Rotating 2D LIDAR System
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(2), 498; doi:10.3390/s18020498

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

1,3,5,* , 5
Satellite Surveying and Mapping Application Center, NASG, Beijing 100048, China
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
Key Laboratory of Satellite Surveying and Mapping Technology and Application, NASG, Beijing 10048, China
School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
School of Surveying and Geographical Science, Liaoning Technical University, Fuxin 123000, China
Author to whom correspondence should be addressed.
Received: 14 December 2017 / Revised: 1 February 2018 / Accepted: 2 February 2018 / Published: 7 February 2018
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [22628 KB, uploaded 7 February 2018]   |  


There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. View Full-Text
Keywords: remote-sensing image; super-resolution reconstruction; multi-scale deposed; adaptive detail enhancement remote-sensing image; super-resolution reconstruction; multi-scale deposed; adaptive detail enhancement

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhu, H.; Tang, X.; Xie, J.; Song, W.; Mo, F.; Gao, X. Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement. Sensors 2018, 18, 498.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top