Flotation froth image deblurring algorithm based on disentangled representations

被引:1
作者
Huang, Xianwu [1 ]
Wang, Yuxiao [1 ]
Cao, Zhao [2 ]
Shang, Haili [2 ]
Zhang, Jinshan [2 ]
Yu, Dahua [1 ]
机构
[1] Inner Mongolia Univ Sci & Technol, Sch Informat Engn, Baotou, Peoples R China
[2] Inner Mongolia Univ Sci & Technol, Sch Mines & Coal, Baotou, Peoples R China
关键词
froth images; coal flotation; feature extraction; disentangled representation; image deblurring; ANOMALY DETECTION;
D O I
10.1117/1.JEI.33.3.033011
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
. The deblurring of flotation froth images significantly aids in the characterization of coal flotation and fault diagnosis. Images of froth acquired at a flotation site contain considerable noise and blurring, making feature extraction and segmentation processing difficult. We present an effective method for deblurring froth images based on disentangled representations. Disentangled representation is achieved by separating the content and blur features in the blurred image using a content encoder and a blur encoder. Then, the separated feature vectors are embedded into a deblurring framework to deblur the froth image. The experimental results show that this method achieves a superior deblurring effect on froth images under various conditions, which lays the foundation for the intelligent adjustment of parameters to guide the flotation site.
引用
收藏
页数:15
相关论文
共 109 条
[1]  
Akay S., 2018, ACCV
[2]   Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection [J].
Akcay, Samet ;
Atapour-Abarghouei, Amir ;
Breckon, Toby P. .
2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2019,
[3]   Online monitoring and control of froth flotation systems with machine vision: A review [J].
Aldrich, C. ;
Marais, C. ;
Shean, B. J. ;
Cilliers, J. J. .
INTERNATIONAL JOURNAL OF MINERAL PROCESSING, 2010, 96 (1-4) :1-13
[4]  
[Anonymous], 2015, Code of Federal Regulations-21 CFR PART, P206
[5]   SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [J].
Badrinarayanan, Vijay ;
Kendall, Alex ;
Cipolla, Roberto .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (12) :2481-2495
[6]   Non-Uniform Blind Deblurring by Reblurring [J].
Bahat, Yuval ;
Efrat, Netalee ;
Irani, Michal .
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2017, :3306-3314
[7]   Representation Learning: A Review and New Perspectives [J].
Bengio, Yoshua ;
Courville, Aaron ;
Vincent, Pascal .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (08) :1798-1828
[8]  
Bergmann P., 2019, VISIGRAPP
[9]   Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings [J].
Bergmann, Paul ;
Fauser, Michael ;
Sattlegger, David ;
Steger, Carsten .
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2020, :4182-4191
[10]   MVTec AD - A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection [J].
Bergmann, Paul ;
Fauser, Michael ;
Sattlegger, David ;
Steger, Carsten .
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, :9584-9592