基于双通道卷积神经网络的图像单失真类型判定方法

doi:10.11772/j.issn.1001-9081.2020091362

计算机应用 ›› 2021, Vol. 41 ›› Issue (6): 1761-1766.DOI: 10.11772/j.issn.1001-9081.2020091362

所属专题：多媒体计算与计算机仿真

• 多媒体计算与计算机仿真 • 上一篇下一篇

基于双通道卷积神经网络的图像单失真类型判定方法

闫钧华^1,2, 侯平^1,2, 张寅^1,2, 吕向阳^1,2, 马越^1,2, 王高飞^1,2

1. 空间光电探测与感知工业和信息化部重点实验室(南京航空航天大学), 南京 211106;
2. 南京航空航天大学航天学院, 南京 211106

收稿日期:2020-09-04 修回日期:2020-11-25 出版日期:2021-06-10 发布日期:2020-12-07
通讯作者: 闫钧华
作者简介:闫钧华(1972-),女,陕西兴平人,教授,博士,主要研究方向:图像质量评价、多源信息融合、目标检测、跟踪与识别;侯平(1993-),男,江苏靖江人,硕士研究生,主要研究方向:图像质量评价;张寅(1986-),男,江苏镇江人,讲师,博士,主要研究方向:光电探测信息仿真与处理;吕向阳(1996-),男,河北景县人,硕士研究生,主要研究方向:图像质量评价;马越(1991-),男,江苏淮安人,硕士研究生,主要研究方向:图像质量评价;王高飞(1996-),女,河南新野人,硕士研究生,主要研究方向:图像处理。
基金资助:
国家自然科学基金资助项目（61705104）；中央高校基本科研业务费专项资金资助项目（NJ2020021）；江苏省自然科学基金资助项目（BK20170804）。

Image single distortion type judgment method based on two-channel convolutional neural network

YAN Junhua^1,2, HOU Ping^1,2, ZHANG Yin^1,2, LYU Xiangyang^1,2, MA Yue^1,2, WANG Gaofei^1,2

1. Key Laboratory of Space Photoelectric Detection and Perception, Ministry of Industry and Information Technology(Nanjing University of Aeronautics and Astronautics), Nanjing Jiangsu 211106, China;
2. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 211106, China

Received:2020-09-04 Revised:2020-11-25 Online:2021-06-10 Published:2020-12-07
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61705104), the Fundamental Research Funds for the Central Universities (NJ2020021), the Natural Science Foundation of Jiangsu Province (BK20170804).

摘要/Abstract

摘要： 针对图像单失真类型判定算法对部分失真类型判定精度低的问题，提出了一种基于双通道卷积神经网络（CNN）的图像单失真类型判定方法。首先，对图像进行裁剪以得到固定尺寸的图像块，并对图像块进行Haar小波变换从而得到高频信息图；然后，将图像块与对应的高频信息图分别输入到不同通道卷积层中以提取深层特征图后，对深层特征进行融合并输入到全连接层中；最后，将全连接层最后一层的值输入到Softmax函数分类器中得到图像单失真类型概率分布。LIVE数据库上的实验结果表明，所提方法的图像单失真类型判定准确率达到了95.21%，并且对JPEG2000和快速衰落失真这两种失真类型的判定精度相较用于对比的其他五种图像单失真类型判定方法分别提升了至少6.69个百分点和2.46个百分点。所提方法能够准确地判定出图像中存在的单失真类型。

关键词: 单失真类型, 卷积神经网络, 小波变换, 双通道, 高频信息图

Abstract: In order to solve the problem of low accuracy of some distortion types judgment by image single distortion type judgment algorithm, an image single distortion type judgment method based on two-channel Convolutional Neural Network (CNN) was proposed. Firstly, the fixed size image block was obtained by cropping the image, and the high-frequency information map was obtained by Haar wavelet transform of the image block. Then, the image block and the corresponding high-frequency information map were respectively input into the convolutional layers of different channels to extract the deep feature map, and the deep features were fused and input into the fully connected layer. Finally, the values of the last layer of the fully connected layer were input into the Softmax function classifier to obtain the probability distribution of the single distortion type of the image. Experimental results on LIVE database show that, the proposed method has the image single distortion type judgement accuracy up to 95.21%, and compared with five other image single distortion type judgment methods for comparison, the proposed method has the accuracies for judging JPEG2000 and fast fading distortions improved by at least 6.69 percentage points and 2.46 percentage points respectively. The proposed method can accurately identify the single distortion type in the image.

Key words: single distortion type, Convolutional Neural Network (CNN), wavelet transform, two-channel, high-frequency information map

中图分类号:

TP391.41

闫钧华, 侯平, 张寅, 吕向阳, 马越, 王高飞. 基于双通道卷积神经网络的图像单失真类型判定方法[J]. 计算机应用, 2021, 41(6): 1761-1766.

YAN Junhua, HOU Ping, ZHANG Yin, LYU Xiangyang, MA Yue, WANG Gaofei. Image single distortion type judgment method based on two-channel convolutional neural network[J]. Journal of Computer Applications, 2021, 41(6): 1761-1766.

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基于双通道卷积神经网络的图像单失真类型判定方法

Image single distortion type judgment method based on two-channel convolutional neural network

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