基于模型驱动反卷积网络的大视场光片图像恢复  

Large Field-of-View Light-Sheet Image Reconstruction Based on Model-Driven Deconvolutional Network

作　　者：吴浩洋 赵晓君 杨孝全 Wu Haoyang;Zhao Xiaojun;Yang Xiaoquan(School of Biomedical Engineering,Hainan University,Haikou 570200,Hainan,China;Wuhan National Laboratory for Optoelectronics,Huazhong University of Science and Technology,Wuhan 430074,Hubei,China)

机构地区：[1]海南大学生物医学工程学院,海南海口570200 [2]华中科技大学武汉光电国家研究中心,湖北武汉430074

出　　处：《激光与光电子学进展》2025年第2期465-473,共9页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2022YEF0203200)。

摘　　要：光片荧光显微成像系统已广泛应用于大体积生物样本成像。然而随着光片系统视场的扩大,成像会在整个视野范围内产生空间不均匀的退化,传统的模型驱动方法和深度学习方法均具有空间不变性,无法直接消除这种退化。在模型驱动的反卷积网络中引入位置信息,通过在训练时随机选择退化规律不同的训练图像对并在图像恢复时采取分块对应重建的方法,实现了位置相关的模型驱动反卷积网络。实验结果表明,该网络可以实现对大视场光片图像的快速反卷积,并提高了图像处理效率、图像质量和视场内图像质量的均匀性。Light-sheet fluorescence microscopy imaging systems are extensively used for imaging large-volume biological samples.However,as the field of view of the optical system expands,imaging will exhibit spatially uneven degradation throughout the entire field of view.Conventional model-driven and deep learning approaches exhibit spatial invariance,making it challenging to directly address this degradation.A position-dependent model-driven deconvolution network is developed by introducing positional information into the model-driven deconvolution network,which is achieved by randomly selecting training image pairs with different degradation patterns during training and using block-based reconstruction techniques during image restoration.The experimental results reveal that the network facilitates rapid deconvolution of large field-of-view optical images,thereby considerably enhancing image processing efficiency,image quality,and the uniformity of image quality within the field of view.

关 键 词：模型驱动反卷积网络 光片荧光显微术 位置相关 图像恢复 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]