基于流模型的荧光图像超分辨率  

Flow⁃Based Model for Fluorescence Image Super⁃Resolution

作　　者：范骏超 苗芸芸 毕秀丽 肖斌[1] 黄小帅 Fan Junchao;MiaoYunyun;Bi XiuLi;Xiao Bin;Huang Xiaoshuai(Chongqing Key Laboratory of Image Cognition,College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Biomedical Engineering Department,Peking University,Beijing 100191,China)

机构地区：[1]重庆邮电大学计算机科学与技术学院重庆市图像认知重点实验室,重庆400065 [2]北京大学跨学部生物医学工程系,北京100191

出　　处：《中国激光》2024年第15期27-37,共11页Chinese Journal of Lasers

基　　金：国家重点研发计划(2022YFF0712503,2021YFA1100201);国家自然科学基金(62103071);重庆市自然科学基金(cstc2021jcyj-msxmX0526,sl202100000288)。

摘　　要：荧光图像超分辨率方法可以将低分辨率荧光图像重建为超分辨率图像。近几年来,基于深度学习的方法通过学习配对的高/低分辨率图像间的映射函数,取得了较好性能。然而,一幅低分辨率荧光图像可能由不同的超分辨图像退化而来,这使得荧光图像超分辨率任务属于欠定问题。现有的深度学习方法通常学习一个确定性的映射函数,而忽略了超分辨率任务的欠定特征。针对这一问题,提出了一种基于流模型的荧光图像超分辨率方法。该方法在给定低分辨率图像的条件下,可以重建出一系列超分辨率图像的分布,更符合超分辨率任务的欠定性质。此外,进一步提出了频域-空域联合注意力模块,用于提取荧光图像的潜在特征,使网络能够充分利用数据在时间和空间上的先验分布,从而获得更准确的图像重建结果。实验结果表明,此方法重建图像的感知指标和重建效果均有所提升。Objective Existing deep learning-based methods for fluorescence image super-resolution can be broadly classified into two categories:those guided by peak signal-to-noise ratio(PSNR)and those guided by perceptual considerations.The former tends to produce excessively smoothed prediction results while the latter mitigates the over smoothing issue considerably;however,both categories overlook the ill-posed nature of the super-resolution task.This study proposes a fluorescence image super-resolution method based on flow models capable of reconstructing multiple realistic super-resolution images that align with the ill-posed nature of super-resolution tasks.Moreover,microscopy imaging is conducted in continuous time sequences naturally containing temporal information.However,current methods often focus solely on individual image frames for super-resolution reconstruction,completely disregarding the temporal information between adjacent frames.Additionally,structures in similar biological samples exhibit a certain degree of similarity,and fluorescence images collected possess internal self-similarity in the spatial domain.To fully leverage the temporal and spatial information present in fluorescence images,this study proposes a frequency-and spatial-domain joint attention module.This module aims to focus on features that significantly contribute to the prediction results,obtaining more accurate reconstruction outcomes.Similar to most supervised learning methods,our approach has a limitation in that it requires labeled paired image sets for training the network model.Generalization performance may significantly decline when applying the model to a test set with a distribution different from the training set.Acquiring labeled paired training data is not always feasible in practical applications.Therefore,future work may need to address the challenge of cross-dataset super-resolution reconstruction,considering optimization strategies and network improvements from a domain adaptation perspective.Methods This study introduc

关 键 词：深度学习 超分辨率 流模型 重建 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]