多孔径压缩编码超分辨率大视场成像方法

多孔径成像是一种融合了仿生复眼视觉的新型成像方法，具有小型化、大视场、高分辨率等多种优势，但由于每个子孔径对应的单元图像分辨率过低，导致其成像质量和视场角的提升十分有限。为了进一步提高成像分辨率和探测视场，基于压缩感知理论设计随机编码模板，并紧贴子孔径放置对入射光场进行调制，通过单次曝光记录编码后的低分辨率单元图像阵列，利用稀疏优化算法，重构所有低分辨率单元图像获得超分辨率大视场图像。理论分析和仿真实验证明了该方法的有效性。该方法不仅能兼顾大视场高分辨率成像，而且大大缩小系统等效焦距，具有薄层结构，体积小而重量轻，可为微光机电一体化系统的研制设计提供借鉴。

Multi-aperture super-resolution and wide-field imaging method using compressive coding

Multi-aperture imaging is a new imaging method combining with compound eye concept, which has a small size, large field of view, high-resolution images reconstruction and other advantages. However, due to the low resolution of sub-images, the improvements for the image resolution and field of view are very limited. A novel imaging method which could achieve both super-resolution and large field of view was proposed. The random coded mask was designed based on the framework of compressive sensing and placed on each sub-aperture. Instead of directly imaging and converging on the image sensor, the incident light field of each sub-aperture would be modulated by the coded mask. Then, the random projections of the input object could be acquired by the low-dimension image sensor within a single exposure. Finally, the sparse representation-based optimization algorithm was applied to reconstruct super-resolution and large field of view images from all low-resolution sub-images, which had more object pixels than the number of pixels of the image sensor. Both the theoretical model and simulation results show the feasibility of the proposed method.