一种非开关型快速随机脉冲噪声降噪算法

doi:10.3969/j.issn.0372-2112.2019.12.023

电子学报 ›› 2019, Vol. 47 ›› Issue (12): 2622-2629.DOI: 10.3969/j.issn.0372-2112.2019.12.023

一种非开关型快速随机脉冲噪声降噪算法

徐少平, 刘婷云, 罗洁, 张贵珍, 李崇禧

南昌大学信息工程学院, 江西南昌 330031

收稿日期:2018-10-08 修回日期:2019-08-13 出版日期:2019-12-25
- 通讯作者:
- 罗洁
- 作者简介:
- 徐少平 男,1976年5月出生于江西省九江市.博士,南昌大学信息工程学院计算机科学与技术系教授,博士生导师.主要研究方向为图形图像处理、机器视觉、虚拟手术仿真等.E-mail:xushaoping@ncu.edu.cn;刘婷云 女,1996年10月出生于江西省抚州市.现为南昌大学硕士研究生,主要研究方向为图像处理与计算机视觉.E-mail:416114517210@email.ncu.edu.cn;张贵珍 女,1993年5月出生于江西省赣州市.现为南昌大学硕士研究生,主要研究方向为图像处理与计算机视觉.E-mail:406130917331@email.ncu.edu.cn;李崇禧 男,1994年8月出生于江西省吉安市.现为南昌大学硕士研究生,主要研究方向为图像处理与计算机视觉.E-mail:406130917315@email.ncu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61662044，No.61163023，No.51765042）; 江西省自然科学基金 (No.20171BAB202017）

A Fast Non-switching Random-Valued Impulse Noise Denoising Algorithm

XU Shao-ping, LIU Ting-yun, LUO Jie, ZHANG Gui-zhen, LI Chong-xi

School of Information Engineering, Nanchang University, Nanchang, Jiangxi 330031, China

Received:2018-10-08 Revised:2019-08-13 Online:2019-12-25 Published:2019-12-25
- Corresponding author:
- LUO Jie
- Supported by:
- National Natural Science Foundation of China (No.61662044, No.61163023, No.51765042); Natural Science Foundation of Jiangxi Province, China (No.20171BAB202017)

摘要/Abstract

摘要： 为提高现有开关型随机脉冲噪声（Random-Valued Impulse Noise，RVIN）降噪算法的降噪性能，提出了一种基于卷积神经网络的非开关型RVIN快速降噪算法（Fast Non-switching RVIN Denoising Algorithm，FNRDA）.首先，利用噪声检测器随机地检测给定噪声图像中少量不同位置处的像素点；然后，将检测为RVIN噪声点的个数除以被检像素点总数转化为噪声比例值；最后，根据噪声比例值调用相应预先训练好的非开关型卷积神经网络降噪模型，快速且高质量地完成图像降噪任务.实验结果表明：所提出的非开关型FNRDA算法在各噪声比例下的综合性能（降噪效果和执行效率）优于经典的开关型RVIN降噪算法，适用于图像恢复、信号检测、无线通讯等实时系统中.

关键词: 降噪, 随机脉冲噪声, 非开关型, 卷积神经网络, 噪声比例值, 执行效率

Abstract: To improve denoising effect and execution efficiency of the existing switching random-valued impulse noise (RVIN) removal algorithms, we propose a convolutional neural network (CNN)-based fast non-switching RVIN denoising algorithm (FNRDA), which consists of two serial CNN-based modules, i.e., noise detector and denoiser. Specifically, we first use the noise detector to detect some randomly selected pixels of a given noisy image. Then we divide the number of the detected noisy pixels by the total number of detected pixels to convert it into noise ratio, which can be treated as a measure of the distortion level for the given noisy image. Finally, according to the estimated noise ratio, we exploit the corresponding pre-trained non-switching CNN-based denoising model to remove RVIN efficiently with high quality. Experimental results show that, the proposed non-switching RVIN removal algorithm outperforms the classical switching ones in terms of denoising effect and execution efficiency across various noise ratios. This advantage makes it more attractive and practical in the real-time applications such as image restoration, signal detection, wireless communication, etc.

Key words: denoising, random-valued impulse noise, non-switching, convolutional neural network (CNN), noise ratio, computational efficiency

中图分类号:

TP391.41

徐少平, 刘婷云, 罗洁, 等. 一种非开关型快速随机脉冲噪声降噪算法[J]. 电子学报, 2019, 47(12): 2622-2629.

XU Shao-ping, LIU Ting-yun, LUO Jie, et al. A Fast Non-switching Random-Valued Impulse Noise Denoising Algorithm[J]. Acta Electronica Sinica, 2019, 47(12): 2622-2629.

参考文献

[1] XU Q,LI Y H,GUO Y J,et al.Random-valued impulse noise removal using adaptive ranked-ordered impulse detector[J].Journal of Electronic Imaging,2018,27(1):013001.
[2] GARNETT R,HUEGERICH T,CHUI C,et al.A universal noise removal algorithm with an impulse detector[J].IEEE Transactions on Image Processing,2005,14(11):1747-1754.
[3] DONG Y Q,CHAN R H,XU S F.A detection statistic for random-valued impulse noise[J].IEEE Transactions on Image Processing,2007,16(4):1112-1120.
[4] LIU L C,CHEN C L P,ZHOU Y C,et al.A new weighted mean filter with a two-phase detector for removing impulse noise[J].Information Sciences,2015,315(September):1-16.
[5] TURKMEN I.The ANN based detector to remove random-valued impulse noise in images[J].Visual Communication and Image Representation,2016,34(October):28-36.
[6] SOLEIMANY S,BRANCH Q,HAMGHALAM M,et al.A novel random-valued impulse noise detector based on MLP neural network classifier[A].Proceedings of the 2017 Artificial Intelligence and Robotics (IRANOPEN)[C].Piscataway,NJ:IEEE,2017.165-169.
[7] 周飞燕,金林鹏,董军.卷积神经网络研究综述[J].计算机学报,2017,40(6):1229-1251. ZHOU Fei-yan,JIN Lin-peng,DONG Jun.Review of convolutional neural network[J].Chinese Journal of Computers,2017,40(6):1229-1251.(in Chinese)
[8] 李宝奇,贺昱曜,何灵蛟,等.基于全卷积神经网络的非对称并行语义分割模型[J].电子学报,2019,47(5):1058-1064. LI Bao-qi,HE Yu-yao,HE Ling-jiao,et al.Asymmetric parallel semantic segmentation model based on full convolutional neural network[J].Acta Electronica Sinica,2019,47(5):1058-1064.(in Chinese)
[9] 李康,李亚敏,胡学敏,等.基于卷积神经网络的鲁棒高精度目标跟踪算法[J].电子学报,2018,46(9):2087-2093. LI Kang,LI Ya-min,HU Xue-min,et al.A robust and accurate object tracking algorithm based on convolutional neural network[J].Acta Electronica Sinica,2018,46(9):2087-2093.(in Chinese)
[10] ZHANG K,ZUO W M,CHEN Y J,et al.Beyond a Gaussian denoiser:Residual learning of deep CNN for image denoising[J].IEEE Transactions on Image Processing,2016,26(7):3142-3155.
[11] ZHANG K,ZUO W M,ZHANG L.FFDNet:Toward a fast and flexible solution for CNN based image denoising[J].IEEE Transactions on Image Processing,2018,27(9):4608-4622.
[12] LEFKIMMIATIS S.Universal denoising networks:A novel CNN architecture for image denoising[A].Proceedings of the 2018 Computer Vision and Pattern Recognition[C].Piscataway,NJ:IEEE,2018.3204-3213.
[13] RAWAT W,WANG Z H.Deep convolutional neural networks for image classification:A comprehensive review[J].Neural Computation,2017,29(9):2352-2449.
[14] IOFFE S,SZEGEDY C.Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift[EB/OL].https://arxiv.org/abs/1502.03167,2015-03-02/2019-08-10.
[15] ZHANG L,ZHANG L,BOVIK A C.A feature-enriched completely blind image quality evaluator[J].IEEE Transactions on Image Processing,2015,24(8):2579-2591.
[16] PONOMARENKO N,JIN L,IEREMEIEV O,et al.Image database TID2013:Peculiarities,results and perspectives[J].Signal Processing:Image Communication,2015,30(1):57-77.
[17] ARBELAEZ P,MAIRE M,FOWLKES C,et al.Contour detection and hierarchical image segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2011,33(5):898-916.
[18] AKKOUL S,LÉDÉE R,LECONGE R,et al.A new adaptive switching median filter[J].IEEE Signal Processing Letters,2010,17(6):587-590.
[19] WANG Z,ZHANG D.Progressive switching median filter for the removal of impulse noise highly corrupted images[J].IEEE Transactions on Circuits System II:Analog Digit Signal Processing.1999,46(1):78-80.
[20] XIONG B,YIN Z P.A universal denoising framework with a new impulse detector and nonlocal means[J].IEEE Transactions on Image Processing,2012,21(4):1663-1675.
[21] JIN K H,YE J C.Sparse and low-rank decomposition of a Hankel structured matrix for impulse noise removal[J].IEEE Transactions on Image Processing,2018,27(3):1448-1461.
[22] LIU P,FANG R G.Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising[EB/OL].https://arxiv.org/abs/1707.09135,2017-07-28/2019-08-10.
[23] MAO X J,SHEN C H,YANG Y B.Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections[EB/OL].https://arxiv.org/abs/1603.09056,2016-09-01/2019-08-10.
[24] PARK S,YU S,KIM M,et al.Dual autoencoder network for Retinex-based low-light image enhancement[J].IEEE Access,2018,6:22084-22093.

一种非开关型快速随机脉冲噪声降噪算法

A Fast Non-switching Random-Valued Impulse Noise Denoising Algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	黄赟, 张帆, 郭威, 陈立, 羊光. 一种基于数据标准差的卷积神经网络量化方法[J]. 电子学报, 2023, 51(3): 639-647.
[2]	吕杭, 蒋明峰, 李杨, 张鞠成, 王志康. 基于混合时频域特征的卷积神经网络心律失常分类方法的研究[J]. 电子学报, 2023, 51(3): 701-711.
[3]	许新征, 李杉. 基于特征膨胀卷积模块的轻量化技术研究[J]. 电子学报, 2023, 51(2): 355-364.
[4]	丁琪, 田萱, 孙国栋. 基于注意力增强的热点感知新闻推荐模型[J]. 电子学报, 2023, 51(1): 93-104.
[5]	张永梅, 孙捷. 基于动静态特征双输入神经网络的咳嗽声诊断COVID-19算法[J]. 电子学报, 2023, 51(1): 202-212.
[6]	袁海英, 成君鹏, 曾智勇, 武延瑞. Mobile_BLNet：基于Big-Little Net的轻量级卷积神经网络优化设计[J]. 电子学报, 2023, 51(1): 180-191.
[7]	吴靖, 叶晓晶, 黄峰, 陈丽琼, 王志锋, 刘文犀. 基于深度学习的单帧图像超分辨率重建综述[J]. 电子学报, 2022, 50(9): 2265-2294.
[8]	毛国君, 王者浩, 黄山, 王翔. 基于剪边策略的图残差卷积深层网络模型[J]. 电子学报, 2022, 50(9): 2205-2214.
[9]	袁海英, 曾智勇, 成君鹏. 面向灵活并行度的稀疏卷积神经网络加速器[J]. 电子学报, 2022, 50(8): 1811-1818.
[10]	徐少平, 李芬, 陈孝国, 陈晓军, 江顺亮. 一种利用改进深度图像先验构建的图像降噪模型[J]. 电子学报, 2022, 50(7): 1573-1578.
[11]	王相海, 赵晓阳, 王鑫莹, 赵克云, 宋传鸣. 非抽取小波边缘学习深度残差网络的单幅图像超分辨率重建[J]. 电子学报, 2022, 50(7): 1753-1765.
[12]	张志文, 刘天歌, 聂鹏举. 基于实景数据增强和双路径融合网络的实时街景语义分割算法[J]. 电子学报, 2022, 50(7): 1609-1620.
[13]	丁毅, 沈薇, 李海生, 钟琼慧, 田明宇, 李洁. 面向CNN的区块链可信隐私服务计算模型[J]. 电子学报, 2022, 50(6): 1399-1409.
[14]	张波, 陆云杰, 秦东明, 邹国建. 一种卷积自编码深度学习的空气污染多站点联合预测模型[J]. 电子学报, 2022, 50(6): 1410-1427.
[15]	张云, 化青龙, 姜义成, 徐丹. 基于混合型复数域卷积神经网络的三维转动舰船目标识别[J]. 电子学报, 2022, 50(5): 1042-1049.