基于cGAN的下采样LG谱图像优化重建  

Optimal image reconstruction from down-sampled LG spectrum based on cGAN

作　　者：叶皓 王麓懿 吴雪炜 张勇 Ye Hao;Wang Luyi;Wu Xuewei;Zhang Yong(National Laboratory of Solid State Microstructures,School of Physics,Nanjing University,Nanjing,210093,China;College of Engineering and Applied Sciences,Nanjing University,Nanjing,210023,China)

机构地区：[1]固体微结构物理国家重点实验室,南京大学物理学院,南京210093 [2]南京大学现代工程与应用科学学院,南京210023

出　　处：《南京大学学报（自然科学版）》2023年第5期752-758,共7页Journal of Nanjing University（Natural Science）

基　　金：中央高校基本业务费(021314380220);南京大学技术创新基金(020414913416)。

摘　　要：对于复杂图像的拉盖尔高斯(Laguerre-Gaussian,LG)谱成像,因为满足奈奎斯特采样率的高阶LG模式系数无法测得,重建图像的失真不可避免,而神经网络算法通过先验学习,可以对失真图像实现较为清晰的复原.提出基于条件生成对抗网络(Conditional Generative Adversarial Nets,cGAN)的图像优化重建方法,在处理下采样的LG谱单像素成像和旋转运动模糊图像中均取得了较好的效果.在1.87%的LG谱采样率下,该方法能将Kaggle数据集人像二值图像的结构相似性(Structural Similarity,SSIM)指数提升至0.8以上,和经典图像去噪算法相比有显著提升.For Laguerre-Gaussian(LG)spectral imaging under Nyquist sampling rate,the reconstructed images are generally distorted because it is difficult to measure the higher-order LG mode coefficients.The neural network algorithm can be used to restore these images through prior learning.In this paper,we propose an optimal image reconstruction method based on Conditional Generative Adversarial Nets(cGAN),which works well in down-sampled LG spectral single-pixel imaging and rotational motion blur recovery in LG spectral domain.We use the portrait binary images from Kaggle dataset as an example.At a sampling rate of 1.87%,the structural similarity(SSIM)index by using our method reaches 0.8 and above,which is significantly improved comparing with classical image denoising algorithms.

关 键 词：频谱下采样 拉盖尔高斯模式 cGAN 图像优化重建 单像素成像 旋转运动模糊图像复原 

分 类 号：O439[机械工程—光学工程]