Convolutional Neural Network for Extracting 3D Point Clouds of Fibrous Web From Multi-Focus Images

Abstract

This paper presents a new method for extracting 3D point clouds from multi-focus images of a fibrous web acquired on an optical microscope to analyze microscopic structures of a fibrous web. The algorithm consists of two major parts: (1) utilizing a convolutional neural network (CNN) to extract in-focus objects from multi-focus images, and (2) a depth identification module (DIM) which is a frequency domain-based model used to identify the depths of object points. The network, namely the multi-focus image deblurring network (MIDN), was designed by introducing gradient features into the network to deblur images and generate the ranges of focal depths of object points. Based on the results of MIDN, DIM was constructed to calculates the focal plane depth for each point. The experiments show that the combination of MIDN and DIM provides a practical way to generate complete, accurate 3D structures of nonwoven.

Keywords

• Image reconstruction
• optical microscopy
• artificial neural networks
• machine vision