基于深度学习的光场显微像差校正  

Light Field Microscopic Aberration Correction Based on Deep Learning

作　　者：王长淼 李晖[1,2] 张水平 吴云韬 Wang Changmiao;Li Hui;Zhang Shuiping;Wu Yuntao(School of Computer Science and Engineering,Wuhan Institute of Technology,Wuhan 430205,Hubei,China;China Hubei Key Laboratory of Intelligent Robot,Wuhan 430205,Hubei,China)

机构地区：[1]武汉工程大学计算机科学与工程学院,湖北武汉430205 [2]智能机器人湖北省重点实验室,湖北武汉430205

出　　处：《光学学报》2024年第14期82-91,共10页Acta Optica Sinica

基　　金：国家自然科学基金(51703071,61771353);武汉市知识创新计划-基础研究(2022010801010350);智能机器人湖北省重点实验室(HBIRL2022203);信息探测与处理山西省重点实验室开放基金(2023001);教育部数码激光成像与显示工程研究中心开放课题(SDGC2134);武汉工程大学研究生创新基金(CX2022329,CX2022347);武汉工程大学校长基金(XZJJ2023033)。

摘　　要：由于光场显微镜中透镜的固有缺陷以及样品折射率分布不均匀会引起光学像差,这严重影响光场显微成像的质量。引入光场解耦模块,解析获得光场的相位和强度信息;并设计用于光场显微像差校正的相位-强度双路径网络(PCANet)。实验结果表明,所提出的方法不仅可实现光场显微像差校正,而且重建出的图像具有高分辨率和清晰的细节边缘;相较于其他传统的图像超分辨率网络,所提方法重建结果的峰值信噪比(PSNR)和结构相似性指数(SSIM)分别提高了15.4%和11.7%。本研究为低成本光场显微像差校正提供了一种高效的方案。Objective Light field microscopy(LFM) is widely employed in real-time cellular activity observation, three-dimensional tissue structure imaging, and organ pathological diagnosis. However, the quality of light field microscopic images is often compromised by inherent lens defects and sample-induced optical aberrations due to variable refractive index distributions.Current aberration correction methods primarily exploit the intensity information of the object, ignoring latent sample phase image data such as thickness variations and 3D morphology. Thus, we introduce a phase-intensity dual-path network(PCANet) designed for high-resolution reconstruction in light field microscopic aberration correction and adopt deep learning to decouple two-dimensional light field microscopic intensity and phase information for enhanced resolution.Experimental results indicate that this deep learning approach effectively replaces light field digital adaptive optics, and achieves aberration correction, high-resolution image reconstruction, and restoration of sample detail edges, thereby recovering the resolution and signal-to-noise ratio of light field microscopic imaging.Methods We propose a PCANet that combines multi-dimensional light field data with a deep learning model to correct imaging aberrations and perform high-resolution reconstruction. The model consists of two serially processed network segments that handle original low-resolution aberrated light field data, ultimately outputting high-resolution reconstruction via light field microscopic decoupling and PCANet modules. This reduces reliance on complex aberration compensation devices, enabling cost-effective and high-resolution light field microscopic reconstruction. The light field microscopic imaging system captures the original low-resolution aberrated data, which is then decoupled by the light field decoupling module into intensity and phase information. The PCANet extracts features from these dimensions, fusing and mining the two-dimensional sample information to enhan

关 键 词：几何光学 光场显微 深度学习 像差校正 高分辨率 相位恢复 

分 类 号：O439[机械工程—光学工程]