计算机科学
稀疏逼近
人工智能
微电网
插值(计算机图形学)
脱模
模式识别(心理学)
旋光法
计算机视觉
关联数组
旋光计
算法
图像处理
图像(数学)
光学
物理
彩色图像
散射
控制(管理)
作者
Junchao Zhang,Haibo Luo,Rongguang Liang,A A Ahmed,Xiangyue Zhang,Hui Bu,Zheng Chang
出处
期刊:Optics Letters
[The Optical Society]
日期:2018-07-06
卷期号:43 (14): 3265-3265
被引量:34
摘要
To address the key image interpolation issue in microgrid polarimeters, we propose a machine learning model based on sparse representation. The sparsity and non-local self-similarity priors are used as regularization terms to enhance the stability of an interpolation model. Moreover, to make the best of the correlation among different polarization orientations, patches of different polarization channels are joined to learn adaptive sub-dictionary. Synthetic and real images are used to evaluate the interpolated performance. The experimental results demonstrate that our proposed method achieves state-of-the-art results in terms of quantitative measures and visual quality.
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