基于MDLatLRR和KPCA的光场图像全聚焦融合  

Light Field All-in-focus Image Fusion Based on MDLatLRR and KPCA

作　　者：黄泽丰 杨莘[1] 邓慧萍[1] 李青松 HUANG Zefeng;YANG Shen;DENG Huiping;LI Qingson(School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学信息科学与工程学院,武汉430081

出　　处：《光子学报》2023年第4期247-261,共15页Acta Photonica Sinica

基　　金：国家自然科学基金(No.61702384)。

摘　　要：为了提升光场成像的空间分辨率,结合光场图像数字重聚焦与多聚焦图像融合,提出了一种基于多尺度潜在低秩分解和核主成分分析的光场图像全聚焦融合算法。首先,对光场图像进行数字重聚焦得到重聚焦图像,然后对各重聚焦图像进行多尺度分解提取出基础层和显著层,对基础层、显著层分别采用局部梯度差值加权算法和多尺度梯度域显著性提取算法计算相应的特征系数;其次,联立基础层和各显著层的特征系数矩阵,然后用核主成分分析进行降维融合得到融合特征系数矩阵,使得经融合特征系数生成的聚焦决策图能充分考虑基础层和显著层的特征信息;最后,用聚焦决策图引导重聚焦图像进行全聚焦融合。实验结果表明,该算法与传统方法相比在视觉效果和边缘信息丰富度上具有更优表现,所生成的光场全聚焦图像具有更高的分辨率和更好的视觉效果。The imaging of a light field camera can retain the spatial and angular information of light,therefore,different from traditional two-dimensional imaging,the light field camera can capture the light field directly in one shot,but it will sacrifice the spatial resolution and angular resolution of the image,so the quality of the image obtained is lower than that of the image generated by the native image sensor.This problem has prevented the application of light field imaging from gaining popularity.The development of multi-focus image fusion technology and digital refocusing technology provides ideas for improving the resolution of light field imaging.To improve the spatial resolution of light field imaging,we propose a fullfocus fusion algorithm of light field image based on multi-scale latent low-rank decomposition and kernel principal component analysis by combining digital refocusing of light field image with multi-focus image fusion.First,by reprojecting the light field,the light is projected from the original focusing plane to the refocusing plane to generate a refocusing image with the focusing region,defocus region and blurred boundary region.After digitally refocusing,the spatial resolution of the light field image located in the focusing area is greatly improved.To extract the focus area accurately,the multi-level image decomposition method based on latent low-rank representation is used to decompose each refocusing image into a base layer and several saliency layers.Then,a two-region image sharpness extraction algorithm is used to calculate the image sharpness of the base layer,and a multi-scale saliency extraction algorithm is used to extract the visual saliency of the saliency layer′s gradient domain.Secondly,the feature coefficient matrices of the base layer and each saliency layer are reshaped and concatenated.The kernel principal component analysis is used for dimension reduction fusion to obtain the fusion feature coefficient matrix which retains the feature information of both the base layer and

关 键 词：光场 全聚焦图像融合 数字重聚焦 多尺度潜在低秩分解 核主成分分析 

分 类 号：TN911.73[电子电信—通信与信息系统]