EANet: Depth Estimation Based on EPI of Light Field.
The light field is an important way to record the spatial information of the target scene. The purpose of this paper is to obtain depth information through the processing of light field information and provide a basis for intelligent medical treatment. In this paper, we first design an attention mod...
| Publicado en: | BioMed Research International pp. 1 - 11 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/28/2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154359439&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154359439 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/28/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154359439 154359439 154359439 10.1155/2021/8293151 154359439 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: EANet: Depth Estimation Based on EPI of Light Field. aug: au: Du, Yunzhang Zhang, Qian Hua, Dingkang Hou, Jiaqi Wang, Bin Zhu, Sulei Zhang, Yan Fang, Yun affil: School of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China sug: subj: Light Neural Networks (Computer) Photography Image Processing, Computer Assisted Quantitative Studies Descriptive Statistics ab: The light field is an important way to record the spatial information of the target scene. The purpose of this paper is to obtain depth information through the processing of light field information and provide a basis for intelligent medical treatment. In this paper, we first design an attention module to extract the features of light field images and connect all the features as a feature map to generate an attention image. Then, the attention map is integrated with the convolution layer in the neural network in the form of weights to enhance the weight of the subaperture viewpoint, which is more meaningful for depth estimation. Finally, the obtained initial depth results were optimized. The experimental results show that the MSE, PSNR, and SSIM of the depth map obtained by this method are increased by about 13%, 10 dB, and 4%, respectively, in some scenarios with good performance. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|