An approach for ordered dither using artificial neural network

被引：0
|
作者：
Chatterjee, Arpitam [1 ]
Tudu, Bipan [2 ]
Paul, Kanai Chandra [1 ]
机构：
[1] Jadavpur Univ, Dept Printing Engn, Kolkata 700032, India
[2] Jadavpur Univ, Dept Instrumentat & Elect Engn, Kolkata 700032, India
来源：
SECOND INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING | 2010年 / 7546卷
关键词：
Ordered dither; digital halftoning; thresholding; artificial neural network (ANN); back-propagation multi layer perceptron (BP-MLP); PSNR; UQI; SSIM;
D O I：
10.1117/12.853179
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Ordered dither is one of the popular techniques for digital halftoning where the original continuous tone image is thresholded against an orderly generated screen matrix. This paper presents a technique to generate the screen matrix using three-layer back-propagation multi layer perceptron (BP-MLP) artificial neural network (ANN) model. The image raw data has been preprocessed prior feeding to the input layer. The output obtained at the hidden layer of the model has been considered as the screen matrix for ordered dither. The results achieved using this technique have been evaluated subjectively as well as objectively using commonly used quality indices like peak signal to noise ratio (PSNR), universal quality index (UQI) and structural similarity index measure (SSIM).
引用
收藏
页数：6
相关论文
共 50 条
  • [31] Prognostics of aluminum electrolytic capacitors using artificial neural network approach
    Khera, Neeraj
    Khan, Shakeb A.
    MICROELECTRONICS RELIABILITY, 2018, 81 : 328 - 336
  • [32] An approach to the calculation of multilayer magnetic shielding using artificial neural network
    Koroglu, S.
    Umurkan, N.
    Kilic, O.
    Attar, F.
    SIMULATION MODELLING PRACTICE AND THEORY, 2009, 17 (07) : 1267 - 1275
  • [33] An artificial neural network approach for predicting hypertension using NHANES data
    Fernando López-Martínez
    Edward Rolando Núñez-Valdez
    Rubén González Crespo
    Vicente García-Díaz
    Scientific Reports, 10
  • [34] Multipoint milling tool supervision using artificial neural network approach
    Dhobale, Nilesh
    Mulik, Sharad
    Jegdeeshwaran, R.
    Ganer, Kaustubh
    MATERIALS TODAY-PROCEEDINGS, 2021, 45 : 1898 - 1903
  • [35] NEW APPROACH FOR ESTIMATION OF POLLUTANT LOAD BY USING ARTIFICIAL NEURAL NETWORK
    Liu, Minghuan
    Yoshimi, Kenji
    Ishikawa, Tadaharu
    Kudo, Kentaro
    ADVANCES IN WATER RESOURCES AND HYDRAULIC ENGINEERING, VOLS 1-6, 2009, : 683 - +
  • [36] Magnetic inverse modelling of a dike using the artificial neural network approach
    Alimoradi, Andisheh
    Angorani, Saeed
    Ebrahimzadeh, Mehrnoosh
    Panahi, Masoud Shariat
    NEAR SURFACE GEOPHYSICS, 2011, 9 (04) : 339 - 347
  • [37] An Evaluation on Offline Signature Verification using Artificial Neural Network Approach
    o-Khalifa, Othman
    Alam, Md. Khorshed
    Abdalla, Aisha Hassan
    2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONICS ENGINEERING (ICCEEE), 2013, : 368 - 371
  • [38] Leak diagnosis in pipelines using a combined artificial neural network approach
    Perez-Perez, E. J.
    Lopez-Estrada, F. R.
    Valencia-Palomo, G.
    Torres, L.
    Puig, V
    Mina-Antonio, J. D.
    CONTROL ENGINEERING PRACTICE, 2021, 107
  • [39] Viscosity prediction of CRM binders using artificial neural network approach
    Xiao, Feipeng
    Putman, Bradley J.
    Amirkhanian, Serji N.
    INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2011, 12 (05) : 485 - 495
  • [40] Knowledge Extraction Using Probabilistic Reasoning: An Artificial Neural Network Approach
    Dabbins, Chelsea
    Fergus, Paul
    2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2015,
12345