高斯—维纳表示下的稠密焦栈图生成方法  

Gaussian-Wiener-based dense focal stack image synthesis

作　　者：王其腾 李志龙 丁新 刘琼 杨铀 Wang Qiteng;Li Zhilong;Ding Xin;Liu Qiong;Yang You(School of Information and Communication,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学电子信息与通信学院,武汉430074

出　　处：《中国图象图形学报》2025年第2期391-405,共15页Journal of Image and Graphics

基　　金：湖北省技术创新计划重点研发专项(2023BAB021);华中科技大学基础研究支持计划资助项目(2023BR023);中央高校基本科研业务费专项资金资助—华中科技大学创新研究院技术创新基金项目(2024JYCXJJ022)。

摘　　要：目的焦栈图像能够扩展光学系统的景深,并为计算摄影、交互式和沉浸式媒体提供灵活的图像表达。然而,受限于光学系统的物理属性和拍摄对象的动态变化,人们往往只能拍摄稀疏的焦栈图像。因此,焦栈图像的稠密化成为当前需要解决的一个难题。为应对上述挑战,提出了一种高斯—维纳表示下的稠密焦栈图生成方法。方法焦栈图像被抽象为高斯—维纳表示,所提出的双向预测模型分别包含双向拟合模块和预测生成模块,在高斯—维纳表示模型的基础上构建双向拟合模型,求解双向预测参数并生成新的焦栈图像。首先,将稀疏焦栈序列的图像按照相同块大小进行分块,并基于此将相邻焦距、相同位置的块组合成块对,以块对为最小单元进行双向预测。其次,在双向预测模块中,块对将用于拟合出最佳双向拟合参数,并基于此求解出预测生成参数,生成新的焦栈图像块。最后,将所有预测生成得到的块进行拼接,得到新的焦栈图像。结果在11组稀疏焦栈图像序列上进行实验,所采用评价指标包括峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structure similarity index measure,SSIM)。11个序列生成结果的平均PSNR为40.861 dB,平均SSIM为0.976。相比于广义高斯和空间坐标两个对比方法,PSNR分别提升了6.503 dB和6.467 dB,SSIM分别提升了0.057和0.092。各序列均值PSNR和SSIM最少提升了3.474 dB和0.012。结论实验结果表明,所提出的双向预测方法可以较好地生成新的焦栈图像,能够在多种以景深为导向的视觉应用中发挥关键作用。Objective In optical imaging systems,the depth of field(DoF)is typically limited by the properties of optical lenses,resulting in the ability to focus only on a limited region of the scene.Thus,expanding the depth of field for optical systems is a challenging task in the community for both academia and industry.For example,in computational photogra⁃phy,when dense focus stack images are captured,photographers can select different focal points and depths of field in postprocessing to achieve the desired artistic effects.In macro-and micro-imaging,dense focus stack images can provide clearer and more detailed images for more accurate analysis and measurement.For interactive and immersive media,dense focus stack images can provide a more realistic and immersive visual experience.However,achieving dense focus stack images also faces some challenges.First,the performance of hardware devices limits the speed and quality of image acqui⁃sition.During the shooting process,the camera needs to adjust the focus quickly and accurately and capture multiple images to build a focus stack.Therefore,high-performance cameras and adaptive autofocus algorithms are required.In addition,changes in the shooting environment,such as object motion or manual operations by photographers,can also introduce image blurring and alignment issues.These challenges are addressed by introducing the block-based Gaussian-Wiener bidirectional prediction model to provide an effective solution.When the image is embedded into blocks and the characteristics of local blocks for prediction are utilized,the computational complexity can be reduced,and the prediction accuracy can be improved.Gaussian-Wiener filtering can smooth prediction results and reduce the impact of artifacts and noise,which can improve image quality.The bidirectional prediction method combines the original sparse focal stack images(FoSIs)with the prediction results to generate dense FoSIs,thereby expanding the DoF of the optical system.The Gaussian-Wiener bidirectional prediction model

关 键 词：焦栈图像(FoSI) 预测模型 高斯—维纳 表示模型 双向预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]