计算机技术与发展2025，Vol.35Issue(3)：26-33,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0338
结合注意力与轴向卷积的荧光图像超分辨率方法
A Super-resolution Method of Fluorescence Images Combining Attention and Axial Convolution
摘要
Abstract
Addressing the challenges of training difficulty on unpaired datasets and excessive computer resource consumption in current fluorescence microscope image super-resolution reconstruction methods,a novel solution,AxialAttention-SRNet,is proposed.This method ingeniously integrates the attention mechanism with 3D axial convolution technology,aiming to optimize the super-resolution processing of fluorescence microscope images.The core of AxialAttention-SRNet lies in its super-resolution network model,which is based on CycleGAN.By integrating 3D axial convolution modules with the Unet network,it effectively captures axial features of images while significantly reducing computational load.Furthermore,to further enhance the quality of image reconstruction,an attention module is introduced into the high-resolution discriminator,significantly boosting its discriminative ability and thereby assisting the model in learning from more informative data.Experiments conducted on datasets such as Thy1-GFPM and MTC-EXM fully demonstrate the out-standing performance of AxialAttention-SRNet.The proposed method not only generates images with superior quality but also features relatively low model complexity,effectively alleviating computational burdens.More importantly,AxialAttention-SRNet exhibits excellent performance on real data,providing an efficient and practical new approach for super-resolution reconstruction of fluorescence microscope images.关键词
循环一致对抗生成网络/轴向卷积/注意力机制/图像生成/UnetKey words
cycle-consistent adversarial generative network/axial convolution/attention mechanism/image generation/Unet分类
信息技术与安全科学引用本文复制引用
陈博伦,周航,王铁军,杨昊..结合注意力与轴向卷积的荧光图像超分辨率方法[J].计算机技术与发展,2025,35(3):26-33,8.基金项目
四川省自然科学基金资助项目(2022NSFSC0964) （2022NSFSC0964）
成都信息工程大学科研基金资助项目(KYTZ202158) （KYTZ202158）
