基于光场 EPI 的深度估计

EANet: Depth Estimation Based on EPI of Light Field.

机构信息

School of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China.

School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang 065000, China.

出版信息

Biomed Res Int. 2021 Dec 28;2021:8293151. doi: 10.1155/2021/8293151. eCollection 2021.

DOI:10.1155/2021/8293151

PMID:34993248

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8727166/

Abstract

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.

摘要

光场是记录目标场景空间信息的一种重要方式。本文旨在通过光场信息的处理获取深度信息，为智能医疗提供依据。本文首先设计一个注意力模块来提取光场图像的特征，并将所有特征连接成一个特征图，生成注意力图像。然后，通过权重的形式将注意力图与神经网络中的卷积层相结合，增强子孔径视点的权重，这对于深度估计更有意义。最后，对得到的初始深度结果进行优化。实验结果表明，在某些场景下，该方法得到的深度图的均方误差（MSE）、峰值信噪比（PSNR）和结构相似性（SSIM）分别提高了约 13%、10dB 和 4%，性能良好。