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Sensors 2018, 18(2), 498; https://doi.org/10.3390/s18020498

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

1,2
,
1,3,4
,
1,3,5,* , 5
,
1
and
1,3,5
1
Satellite Surveying and Mapping Application Center, NASG, Beijing 100048, China
2
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
3
Key Laboratory of Satellite Surveying and Mapping Technology and Application, NASG, Beijing 10048, China
4
School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
5
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]   |  

Abstract

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
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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.

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