实时VR场景下多尺度数字全息成像仿真  

Simulation of Multi-Scale Digital Holography in VR Scene

作　　者：辜昕宇[1] 黎鹏[2] GU Xin-yu;LI Peng(College of Science and Technology,Jiangxi Normal University,Nanchang Jiangxi 332020,China;Nanchang University,Nanchang Jiangxi 332020,China)

机构地区：[1]江西师范大学科学技术学院,江西南昌332020 [2]南昌大学,江西南昌332020

出　　处：《计算机仿真》2024年第5期178-182,共5页Computer Simulation

基　　金：2021江西省高校人文社科课题(YS21210)。

摘　　要：在数字全息成像的过程中,受光电传感器件结构参量、光照强度以及传输方向等因素的影响,导致全息图成像的亮度、分辨率降低。为了提高多尺度数字全息成像的真实度,提出了实时VR场景下多尺度数字全息成像方法。通过定义图像像素点分布的概率函数,计算多尺度数字图像像素的显著性,根据图像像素特征的分布方差,提取出多尺度数字图像像素点。利用小波变换系数识别多尺度数字图像的噪声,基于图像噪声信号的方差分布,精细化处理多尺度数字图像,结合小波反变换,构建多尺度数字图像去噪模型,得到去噪后的图像。根据多尺度数字全息成像模型,得到图像增强的梯度场,在实时VR场景下,引入加权融合的方法,得到图像的高频细节分量,利用图像融合规则,生成多尺度数字图像。实验结果表明,所提方法能够生成VR图像,并将成像真实度和清晰度分别提高到90%以上和92%以上。Due to the influence of the structural parameters of photoelectric sensors,light intensity and transmission direction,the brightness and resolution of the hologram imaging may be reduced in the process of digital holographic imaging.In order to improve the trueness of multi-scale digital holographic imaging,a new method in realtime VR scenes was proposed.Firstly,we calculated the significance of multi-scale digital image pixels by defining the probability function of pixel distribution.According to the distribution variance of pixel characteristics,we extracted the pixels of multi-scale digital images.And then,we used the wavelet transform coefficients to identify the noise of multi-scale digital images.Based on the variance distribution of noise signals,we refined multi-scale digital images.Moreover,we built a denoising model based on the wavelet inverse transform,thus obtaining the denoised images.According to the multi-scale digital holographic imaging model,we could obtain the gradient field of image enhancement.In the real-time VR scene,we used the weighted fusion method to obtain the high-frequency detail components.Finally,based on the fusion rule,multi-scale digital images were generated.Experimental results show that the proposed method can generate VR images,and improve the trueness and clarity of images to more than 90%and 92%respectively.

关 键 词：实时虚拟现实场景 全息成像 像素点提取 去噪模型 多尺度 真实度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]