光电工程, 2020, 47 (2): 180661, 网络出版: 2020-03-06  

基于变分贝叶斯多图像超分辨的平面复眼空间分辨率增强

Spatial resolution enhancement of planar compound eye based on variational Bayesian multi-image super-resolution
闵雷 1,2,3,4,*杨平 1,3,4许冰 1,3,4刘永 2
作者单位
1 中国科学院自适应光学重点实验室, 四川成都 610209
2 电子科技大学光电科学与工程学院, 四川成都 610054
3 中国科学院光电技术研究所, 四川成都 610209
4 中国科学院大学, 北京 100049
摘要
平面复眼成像系统利用多个子孔径对场景进行成像, 由于子孔径大小和图像传感器空间采样率的限制, 各子孔径图像质量较差。如何融合多个子孔径图像来获得高分辨率图像是亟需解决的问题。多图像超分辨理论利用多幅具有互补信息的图像来重构高空间分辨率图像, 然而现有理论通常采用过于简化的运动模型, 这种简化的运动模型对平面复眼成像并不完全适用。若直接把现有多图像超分辨理论用于平面复眼分辨率增强, 不准确的相对运动估计将降低图像分辨率增强性能。针对这些问题, 本文在变分贝叶斯框架下改进了现有多图像超分辨理论中的运动模型, 并把导出的联合估计算法用于平面复眼分辨率增强。仿真数据实验和真实复眼数据实验验证了推荐方法的正确性和有效性。
Abstract
The planar compound eye imaging system uses multiple sub-apertures to image the scene. Due to the constraint of the imaging sub-aperture size and spatial sampling rate of the image sensor, the image quality of each sub-aperture is low. How to fuse multiple sub-aperture images for a high-resolution image is an urgent problem. Multi-image super-resolution theory uses multiple images with complementary information to reconstruct high spatial resolution image. However, existing theories usually adopt the oversimplified motion model which is not suitable for planar compound eye imaging. If the existing multi-image super-resolution theory is directly applied to the resolution enhancement of the planar compound eye, the inaccurate motion estimation will reduce the performance of image resolution enhancement. In order to solve these problems, the motion model of the multi-image super-resolution is improved in the variational Bayesian framework, and the derived joint estimation algorithm is used to enhance the resolution of the planar compound eye. The correctness and effectiveness of the proposed method is verified by the simulation data experiments and the real compound eye data experiments.

闵雷, 杨平, 许冰, 刘永. 基于变分贝叶斯多图像超分辨的平面复眼空间分辨率增强[J]. 光电工程, 2020, 47(2): 180661. Min Lei, Yang Ping, Xu Bing, Liu Yong. Spatial resolution enhancement of planar compound eye based on variational Bayesian multi-image super-resolution[J]. Opto-Electronic Engineering, 2020, 47(2): 180661.

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