[1]牟新刚,陆俊杰,周晓.基于残差编解码网络的红外图像自适应校正算法[J].红外技术,2020,42(9):833-839.[doi:10.11846/j.issn.1001_8891.202009004]
 MOU Xingang,LU Junjie,ZHOU Xiao.Adaptive Correction Algorithm of Infrared Image Based on Encoding and Decoding Residual Network[J].Infrared Technology,2020,42(9):833-839.[doi:10.11846/j.issn.1001_8891.202009004]
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基于残差编解码网络的红外图像自适应校正算法
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第9期
页码:
833-839
栏目:
出版日期:
2020-09-23

文章信息/Info

Title:
Adaptive Correction Algorithm of Infrared Image Based on Encoding and Decoding Residual Network
文章编号:
1001-8891(2020)09-0833-07
作者:
牟新刚陆俊杰周晓
武汉理工大学 机电工程学院
Author(s):
College of Mechanical and Electrical Engineering, Wuhan University of Technology
关键词:
红外图像非均匀性校正多尺度采样残差学习
Keywords:
infrared image non-uniformity multi-scale sampling residual learning
分类号:
TP391.4
DOI:
10.11846/j.issn.1001_8891.202009004
文献标志码:
A
摘要:
针对基于场景的非均匀性校正算法存在非均匀性残余和鬼影等问题,本文提出了一种基于残差编解码网络的红外图像自适应算法。该算法针对自适应校正问题的特点,基于UNet结构,通过多尺度采样学习残差映射生成非均匀性残差图像,加入批标准化和PReLU激活函数提高校正效果,最后使用全局跳跃连接得到最终的校正结果。通过对模拟红外图像序列和真实红外图像序列校正的实验结果表明,相对于目前已有的非均匀性校正算法,该方法在PSNR(Peak Signal to Noise Ratio)和粗糙度的客观数据上都有所提升,主观视觉效果也更加清晰,细节保留程度高。
Abstract:
?Traditional scene-based non-uniformity correction algorithms generally suffer from non-uniformity residuals and ghosts. In view of this, we propose an infrared image adaptive algorithm based on the encoding and decoding residual network. The algorithm focuses on the characteristics of the adaptive correction problem. Following the UNet structure, the residual image is generated through multiscale sampling and learning residual mapping. Batch normalization and PReLU are used to improve the correction effect. Finally, the global skip connection is used to obtain the final correction result. The experimental results of correcting the simulated non-uniform infrared image sequence and the real infrared image sequence showed that this method improved the objective data of the peak signal to noise ratio (PSNR) and roughness, compared with existing non-uniformity correction algorithms. Moreover, the subjective visual effect was clearer, and the degree of detail retention was high.

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备注/Memo

备注/Memo:
收稿日期:2020-02-19;修订日期:2020-07-26.
作者简介:牟新刚(1982-),男,博士,副教授,主要研究方向光电成像与信息处理、红外图像处理,E-mail:mouxingang@163.com。
基金项目:国家基金项目(61701357),中央高校基本科研业务费专项资金资助(183204007)。

更新日期/Last Update: 2020-09-17