摘要
现有的光场图像显著性检测算法不能有效地衡量聚焦度信息,从而影响了检测目标的完整性,造成信息的冗余和边缘模糊。考虑到焦堆栈不同的图像及全聚焦图像对于显著性预测发挥着不同的作用,提出有效通道注意力(ECA)网络和卷积长短期记忆模型(ConvLSTM)网络组成特征融合模块,在不降低维度的情况下自适应地融合焦堆栈图像和全聚焦图像的特征;然后由交互特征模块(CFM)组成的反馈网络细化信息,消除特征融合之后产生的冗余信息;最后利用ECA网络加权高层特征,更好地突出显著性区域,从而获得更加精确的显著图。所提网络在最新的数据集中,F-measure和平均绝对误差(MAE)分别为0.871和0.049,表现均优于现有的红、绿、蓝(RGB)图像、红、绿、蓝和深度(RGB-D)图像以及光场图像的显著性检测算法。实验结果表明,提出网络可以有效分离焦堆栈图像的前景区域和背景区域,获得较为准确的显著图。
The existing light field image saliency detection algorithms cannot effectively measure the focus information,resulting in an incomplete salient object,information redundancy,and blurred edges.Considering that different slices of the focal stack and the all-focus image play different roles in saliency prediction,this study combines the efficient channel attention(ECA)network and convolutional long short-term memory model(ConvLSTM)network to form a feature fusion network that adaptively fuse the features of the focal stack slices and all-focus images without reducing the dimension;then the feedback network composed of the cross feature module refines the information and eliminates the redundant information generated after the feature fusion;finally,the ECA network is used for weighing the high-level features to better highlight the saliency area to obtain a more accurate saliency map.The network proposed has F-measure and mean absolute error(MAE)of 0.871 and 0.049,respectively,in the most recent data set,which are significantly better than the existing red,green,and blue(RGB)images,red,green,blue,and depth(RGB-D)images,and light field images saliency detection algorithms.The experimental results show that the proposed network can effectively separate the foreground and background regions of the focal stack slices and produce a more accurate saliency map.
作者
梁晓
邓慧萍
向森
吴谨
Liang Xiao;Deng Huiping;Xiang Sen;Wu Jin(School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,Hubei,China;Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,Hubei,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第22期122-130,共9页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61702384,61502357)。
关键词
图像处理
显著性检测
深度学习
光场图像
卷积神经网络
image processing
saliency detection
deep learning
light field image
convolutional neural network
