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MAP super-resolution reconstruction of remote sensing image
- Chinese Journal of Liquid Crystals and Displays Vol. 33, Issue 10, Pages: 884-892(2018)
- 作者机构：
1.中国科学院长春光学精密机械与物理研究所, 吉林 长春 130033
2.中国科学院大学, 北京 100049
- 作者简介：
- 基金信息：
DOI：10.3788/YJYXS20183310.0884
CLC： TP394.1;TH691.9Received：04 June 2018，
Accepted：09 July 2018，
Published：05 October 2018
- 稿件说明：
移动端阅览
Tao LIU, Feng QIAN, Bao ZHANG. MAP super-resolution reconstruction of remote sensing image[J]. Chinese journal of liquid crystals and displays, 2018, 33(10): 884-892.
Tao LIU, Feng QIAN, Bao ZHANG. MAP super-resolution reconstruction of remote sensing image[J]. Chinese journal of liquid crystals and displays, 2018, 33(10): 884-892. DOI： 10.3788/YJYXS20183310.0884.
摘要
遥感根本目的就是获得清晰的高空间分辨率的图像，从而可以进一步地分析处理。为了在遥感测量中获得更高空间分辨率、更高信噪比、更清晰的图像，本文对图像处理领域超分辨算法进行了研究。建立了一套拟合模拟现实的成像系统模型，在这种模型的基础之上，利用最大后验概率系统理论，讨论了现实情况中的运动模糊，噪声等情况，改进了MAP超分辨算法。实验结果表明：使用本文改进的基于MAP理论的Markov随机场约束的多帧超分辨重建算法，可以较好提高超分辨效果，与三次立方插值方法相比，PSNR至少提高约5.1 dB左右，与未改进的MAP方法相比，PSNR提高约0.2 dB左右。本文提出了动态的先验约束方法，给约束函数添加与迭代次数相关的约束项，该改进创新可以加快收敛并且更加逼近真实图像，实验表明该方法收敛速度更快，约束效果良好，更适合实际应用。
Abstract
The fundamental purpose of remote sensing is to obtain clear images with high spatial resolution
which can be further analyzed and processed. In order to obtain higher spatial resolution
higher signal-to-noise ratio and clearer images in remote sensing measurement
this paper studies the superresolution algorithm in image processing field. In the paper we set up a model fitting the real imaging system
use the theory of maximum a posteriori probability system on this model
discuss the motion blur
noise and other degrading factors
and finally improve the MAP superresolution algorithm. The experimental results show that the improved multi-frame superresolution reconstruction algorithm in the paper based on the Markov constraint field which is originated from the MAP theory
can improve the superresolution performance significantly. Using our method
PSNR is at least about 5.1 dB higher than the cubic interpolation method
and is about 0.2 dB higher than the unimproved MAP method. In our paper a dynamic prior constraint method is proposed
by adding constraints associated with the number of iterations to constraint function
the improvement innovation can accelerate convergence and more close to real images. And the experiments show that this method can fasten the convergence
and its constraint effect is good. Above all
it is more suitable for practical application.
关键词
Keywords
references
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