基于深度学习的图像配准方法研究进展综述

doi:10.3969/j.issn.1007-5461.2022.06.006

量子电子学报 ›› 2022, Vol. 39 ›› Issue (6): 899-926.doi: 10.3969/j.issn.1007-5461.2022.06.006

• "光电探测与成像新技术及应用"专辑 • 上一篇下一篇

基于深度学习的图像配准方法研究进展综述

陈建明1,2 , 曾祥津1,2 , 钟丽云1,2 , 邸江磊1,2∗ , 秦玉文1,2∗

( 1 广东工业大学信息工程学院, 先进光子技术研究院, 广东广州 510006; 2 广东省信息光子技术重点实验室, 广东广州 510006 )

收稿日期:2022-07-18 修回日期:2022-08-16 出版日期:2022-11-28 发布日期:2022-12-14
通讯作者: E-mail: jiangleidi@gdut.edu.cn E-mail: E-mail: jiangleidi@gdut.edu.cn
作者简介:陈建明 ( 1999 - ), 湖南衡阳人, 研究生, 主要研究方向为基于深度学习的图像分析与处理。 E-mail: 2112103003@mail2.gdut.edu.cn
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金, 62075183, 62175041), Program for Guangdong Introducing Innovative and Entrepreneurial Teams (广东省珠江人才计划引进创新创业团队项目, 2019ZTO8X340)

Research progress of image registration methods based on deep learning

CHEN Jianming 1,2 , ZENG Xiangjin 1,2 , ZHONG Liyun 1,2 , DI Jianglei 1,2∗ , QIN Yuwen 1,2∗

( 1 Advanced Institute of Photonics Technology, School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China; 2 Guangdong Provincial Key Laboratory of Information Photonics Technology, Guangzhou 510006, China )

Received:2022-07-18 Revised:2022-08-16 Published:2022-11-28 Online:2022-12-14

摘要/Abstract

摘要： 近年来，图像采集设备的高速发展极大地丰富了图像种类和数量，图像配准技术作为图像分析和处理的关键，在图像融合、模式识别和计算机视觉等领域作用日益重要，如何高精度、实时配准已成为该领域的研究重点。与此同时，近年来深度学习技术发展迅速，卷积神经网络在图像表示、特征提取等方面显示出独特优势。本文系统综述了基于深度学习技术实现图像配准的相关研究进展，深入讨论了基于深度迭代配准、全监督图像配准、弱/双重监督图像配准、无监督图像配准等典型的基于深度学习的图像配准方法，总结了相关领域研究人员所面临的共同挑战，并指出了未来可能的研究方向。

关键词: 图像处理, 图像配准, 深度学习, 卷积神经网络

Abstract: In recent years, the rapid development of image acquisition equipment has greatly enriched the types and quantities of images. As the key of image analysis and processing, image registration technology has become increasingly important in the fields of image fusion, pattern recognition and computer vision, and how to register images with high accuracy and in real time has become the focus of research. At the same time, deep learning techniques shine, and convolutional neural networks show unique advantages in image representation and feature extraction. The aim is to provide a systematic review of research on image registration using deep learning techniques. By discussing typical deep learning-based image registration methods from deep iterative registration, fully supervised image registration, weak/dually supervised image registration, and unsupervised image registration, we highlight common challenges faced by related researchers and explore possible future research directions to address these challenges.

Key words: image processing, image registration, deep learning, convolutional neural network

中图分类号:

TP391.41

陈建明, , 曾祥津, , 钟丽云, , 邸江磊, ∗ , 秦玉文, ∗. 基于深度学习的图像配准方法研究进展综述[J]. 量子电子学报, 2022, 39(6): 899-926.

CHEN Jianming , , ZENG Xiangjin , , ZHONG Liyun , , DI Jianglei , ∗ , QIN Yuwen , ∗. Research progress of image registration methods based on deep learning[J]. Chinese Journal of Quantum Electronics, 2022, 39(6): 899-926.

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基于深度学习的图像配准方法研究进展综述

Research progress of image registration methods based on deep learning

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