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
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
  • [41] Knowledge Extraction Using Probabilistic Reasoning: An Artificial Neural Network Approach
    Dabbins, Chelsea
    Fergus, Paul
    2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2015,
  • [42] Prediction of fracture parameters of concrete using an artificial neural network approach
    Xu, Shilang
    Wang, Qingmin
    Lyu, Yao
    Li, Qinghua
    Reinhardt, Hans W.
    ENGINEERING FRACTURE MECHANICS, 2021, 258
  • [43] Performance evaluation of air ejectors using artificial neural network approach
    Pradeep Gupta
    Srisha M V Rao
    Pramod Kumar
    Sādhanā, 48
  • [44] Collaborative supply chain planning using the artificial neural network approach
    Chiu, Matthew
    Lin, Grier
    Journal of Manufacturing Technology Management, 2004, 15 (08) : 787 - 796
  • [45] Vehicular traffic noise modeling using artificial neural network approach
    Kumar, Paras
    Nigam, S. P.
    Kumar, Narotam
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2014, 40 : 111 - 122
  • [46] An integrated approach for protein structure prediction using artificial neural network
    Mathkour, Hassan
    Ahmad, Muneer
    2010 SECOND INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND APPLICATIONS: ICCEA 2010, PROCEEDINGS, VOL 2, 2010, : 484 - 488
  • [47] Modeling Study of Adsorption of Phenol Using Artificial Neural Network Approach
    Meriem, Sediri
    Salah, Hanini
    Maamar, Laidi
    Siham, Abbas Turki
    PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON APPLIED SMART SYSTEMS (ICASS), 2018,
  • [48] Leak diagnosis in pipelines using a combined artificial neural network approach
    Pérez-Pérez, E.J.
    López-Estrada, F.R.
    Valencia-Palomo, G.
    Torres, L.
    Puig, V.
    Mina-Antonio, J.D.
    Control Engineering Practice, 2021, 107
  • [49] Evaluation of Pavement Condition Index Using Artificial Neural Network Approach
    Rajnish Kumar
    Sanjeev Kumar Suman
    Gautam Prakash
    Transportation in Developing Economies, 2021, 7
  • [50] Forecasting the Baltic Dry Index by using an artificial neural network approach
    Sahin, Bekir
    Gurgen, Samet
    Unver, Bedir
    Altin, Ismail
    TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2018, 26 (03) : 1673 - 1684