基于字典学习的大气湍流退化图像复原技术应用

Abstract

Abstract:

为了消除大气湍流对图像的影响, 提高图像质量, 结合稀疏表示理论, 采用字典学习的算法处理大气湍流退化图像。将DCT 过完备字典、K-svd 全局字典和自适应字典的算法应用于图像去噪过程, 并与维纳滤波算法进行比较。结果表明, 该算法能较好地滤除大气湍流退化图像的噪声, 提高图像的峰值信噪比。仿真实验验证了稀疏表示在处理大气湍流退化图像的可行性, 对比传统算法具有更好的去噪性能。

Key words: 大气湍流, 图像复原, DCT 过完备字典, K-svd 字典, 自适应字典

CLC Number:

TP391

XU Yurui, LIU Le, WANG Ganggang, HOU Alin. Restoration of Atmospheric Turbulence Degraded Image Based on Dictionary Learning[J].Journal of Jilin University(Information Science Ed, 2016, 34(1): 153-157.

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Restoration of Atmospheric Turbulence Degraded Image Based on Dictionary Learning

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