基于通道注意力与迁移学习的红外图像超分辨率重建算法  

Super-resolution reconstruction of infrared image based on channel attention and transfer learning

作　　者：孙锐[1,2] 章晗 程志康 张旭东 Sun Rui;Zhang Han;Cheng Zhikang;Zhang Xudong(School of Computer and Information,Hefei University of Technology,Hefei,Anhui 230009,China;Anhui Province Key Laboratory of Industry Safety and Emergency Technology,Hefei,Anhui 230009,China)

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230009 [2]工业安全与应急技术安徽省重点实验室,安徽合肥230009

出　　处：《光电工程》2021年第1期1-13,共13页Opto-Electronic Engineering

基　　金：国家自然科学基金面上项目(61471154,61876057);中央高校基本科研业务费专项资金资助项目(JZ2018YYPY0287)。

摘　　要：针对现有红外图像分辨率低、质量不高的问题,提出了基于通道注意力与迁移学习的红外图像超分辨率重建方法。该方法设计了一个深度卷积神经网络,融入通道注意力机制来增强网络的学习能力,并且使用残差学习方式来减轻梯度爆炸或消失问题,加速网络的收敛。考虑到高质量的红外图像难以采集、数目不足的情况,将网络的训练分成两步:第一步使用自然图像来预训练网络模型,第二步利用迁移学习的知识,用较少数量的高质量红外图像对预训练的模型参数进行迁移微调,使模型对红外图像的重建效果更优。最后,加入多尺度细节滤波器来提升红外重建图像的视觉效果。在Set5、Set14数据集以及红外图像上的实验表明,融入通道注意力机制和残差学习方法,均能提升超分辨率重建的效果,迁移微调能很好地解决红外样本数量不足的问题,而多尺度细节提升滤波则能提升重建图像的细节,增大信息量。A super-resolution reconstruction method of infrared images based on channel attention and transfer learning was proposed to solve the problems of low resolution and low quality of infrared images.In this method,a deep convolutional neural network is designed to enhance the learning ability of the network by introducing the channel attention mechanism,and the residual learning method is used to mitigate the problem of gradient explosion or disappearance and to accelerate the convergence of the network.Because high-quality infrared images are difficult to collect and insufficient in number,so this method is divided into two steps:the first step is to use natural images to pre-train the neural network model,and the second step is to use transfer learning knowledge to fine-tune the pre-trained model’s parameters with a small number of high-quality infrared images to make the model better in reconstructing the infrared image.Finally,a multi-scale detail boosting filter is added to improve the visual effect of the reconstructed infrared image.Experiments on Set5 and Set14 datasets as well as infrared images show that the deepening network depth and introducing channel attention mechanism can improve the effect of super-resolution reconstruction,transfer learning can well solve the problem of insufficient number of infrared image samples,and multi-scale detail boosting filter can improve the details and increase the amount of information of the reconstruction image.

关 键 词：超分辨率 红外图像 卷积神经网络 注意力 迁移学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TB866[自动化与计算机技术—计算机科学与技术]