摘要
为了避免传统全息重建方法步骤繁杂且重建效果易受噪声干扰等问题,采用一种改进的语义分割U型网络用于全息图超分辨重建工作。首先引入新型的端侧神经网络,用来充分获取更多的图像语义信息,增强网络学习性能;其次加入深度神经卷积网络的高效通道注意力以提高网络关注全息图中细节信息的能力,进一步提升网络精度,同时采用带泄露修正线性单元作为激活函数,加快网络收敛;并采用血细胞和鸡血细胞的低分辨率全息图进行训练,取得了超分辨重建强度和位相图。结果表明,改进网络能够快速重建出细节信息丰富、边缘纹理清晰、背景平坦的位相和强度图像,血细胞强度重建图的结构相似性指数和峰值信噪比分别达到0.9613和27.38,同时可对不同尺度的全息图进行重建。该研究为使用深度学习方法提高全息图质量提供了参考。
In order to avoid the problems of complicated steps and noise interference of traditional holographic reconstruction methods,an improved semantic segmentation U-Net network was used for super-resolution reconstruction of holograms.Firstly,a novel end-to-end neural network was introduced to fully acquire more semantic information of images and to enhance the performance of network learning.Secondly,the efficient channel attention(ECA)of deep neural convolutional network was added to improve the ability of focusing on details in the holograms,and to further improve the accuracy of the network.The leaky rectified linear units(LeakyReLU)was used as the activation function to accelerate the network convergence.Using the low resolution holograms of blood cells and chicken blood cells for training,the super-resolution reconstruction intensity and phase map were obtained.The results show that the improved network can quickly reconstruct the phase and intensity images with rich details,clear edge texture and flat background.The structure similarity index measure(SSIM)and peak signal-to-noise ratio(PSNR)of the blood cell intensity reconstruction images are 0.9613 and 27.38,respectively.Meanwhile,the holograms of different scales can be reconstructed.This study provides a reference for using deep learning to improve the quality of holograms.
作者
裴瑞景
王硕
王华英
PEI Ruijing;WANG Shuo;WANG Huaying(School of Mathematics and Physics Science and Engineering,Hebei University of Engineering,Handan 056038,China;Hebei Province Computational Optical Imaging and Photoelectric Detection Technology Innovation Center,Handan 056038,China)
出处
《激光技术》
CAS
CSCD
北大核心
2023年第4期485-491,共7页
Laser Technology
基金
国家自然科学基金资助项目(62175059)
河北省自然科学基金重点资助项目(2018402285)。
关键词
全息
超分辨重建
通道注意力机制
端侧神经网络
多尺度重建
holography
super-resolution reconstruction
channel attention mechanism
end-side neural network
multiscale reconstruction
