Spatial Modulation Full Polarization Computing Imaging Super-Resolution via Scene Transfer

Q3 Computer Science
Guoming Xu, Hongwu Yuan, Mo-gen Xue, Feng Wang, Jie Wang
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引用次数: 0

Abstract

The spatial modulation full polarization computing imaging can obtain both visible and infrared channel images synchronously. However, due to the limitation of the detector, the spatial resolution of the two channels is inconsistent, which brings inconvenience to the following image fusion and target detection process. A spatial modulation computing imaging super-resolution method via scene feature transfer learning is proposed. Firstly, the scene feature transfer model is constructed. The model is based on the analysis of the spatial modulation computing imaging characteristics of different source images in the same scene. Secondly, the convolutional neural networks (CNN) structure of scene transfer is improved and the rectified linear unit 第 9 期 徐国明, 等: 空间调制全偏振计算成像场景迁移超分辨率方法 1441 activation function is selected. At the same time, the spatial resolution consistency constraint is added. Then, the optimal spectral transfer response learning strategy is designed and added to the super-resolution network as the front-end input. Finally, the parameters of spectral transfer response optimization and full polarization super-resolution reconstruction are learned together to obtain the high-resolution polarization image. Some super-resolution experiments with scale factors 2 and 3 are carried out using simulation data and system data of the actual imaging system. The experiments result is evaluated by multiple indicators with three aspects such as subjective visual effect, objective quantitative index and polarization parameter analysis results. In the visual effect, the method can keep the object contour and suppress the noise interference. On the 16 objective index data, the method obtains 10 better, 3 equal and 3 lower results compared with the others. The results verify the effectiveness of the method and also provide data support for calibration correction of imaging system.
基于场景转换的空间调制全极化计算成像超分辨率
空间调制全偏振计算成像可以同时获得可见光和红外通道图像。然而,由于检测器的限制,两个通道的空间分辨率不一致,这给后续的图像融合和目标检测过程带来了不便。提出了一种基于场景特征迁移学习的空间调制计算成像超分辨率方法。首先,构建了场景特征转移模型。该模型是在分析空间调制的基础上计算不同源图像在同一场景中的成像特性。其次,对场景转移的卷积神经网络(CNN)结构进行了改进,得到了校正后的线性单元第 9期 徐国明, 等: 空间调制全偏振计算成像场景迁移超分辨率方法 1441激活功能被选择。同时,增加了空间分辨率一致性约束。然后,设计了最优谱转移响应学习策略,并将其作为前端输入添加到超分辨率网络中。最后,将光谱传输响应优化和全偏振超分辨率重建的参数结合起来,得到高分辨率的偏振图像。利用仿真数据和实际成像系统的系统数据,进行了一些比例因子为2和3的超分辨率实验。实验结果采用主观视觉效果、客观定量指标和偏振参数分析结果三个方面的多指标进行评价。在视觉效果上,该方法可以保持物体的轮廓,抑制噪声干扰。在16个客观指标数据上,与其他方法相比,该方法获得了10个较好、3个相等和3个较低的结果。结果验证了该方法的有效性,也为成像系统的标定校正提供了数据支持。
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来源期刊
计算机辅助设计与图形学学报
计算机辅助设计与图形学学报 Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.20
自引率
0.00%
发文量
6833
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