Convolutional Autoencoders for Image Comparison in Printing Industry Quality Control

An important problem in the printing industry is the automation of the quality control of the printed materials and timely detection of relevant printing faults. Image quality assessment can be used to support the quality evaluation of the printed material based on comparison of initial input image and a camera captured image of the printed result. Besides the utilization of histogram based analyses and statistical evaluation metrics such as mean squared error and structural similarity index, deep learning techniques can also be applied for image comparison. The paper presents a deep learning approach based on convolutional autoencoder for image comparison utilizing data augmentation and clustering in order to evaluate the quality of printed materials.

[1]  Chen Youping,et al.  Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm , 2021, Complex..

[2]  Jungsuk Kim,et al.  Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder , 2021, Sensors.

[3]  Iryna Pikh,et al.  Forecasting Assessment of Printing Process Quality , 2020, ISDMCI.

[4]  Jan P. Allebach,et al.  Deep Learning for Printed Mottle Defect Grading , 2020, IMAWM.

[5]  Mohammad Shorif Uddin,et al.  Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study , 2019, Journal of Computer and Communications.

[6]  Olivier Bachem,et al.  Recent Advances in Autoencoder-Based Representation Learning , 2018, ArXiv.

[7]  Alex A. Volinsky,et al.  Control of the offset printing image quality indices , 2017 .

[8]  A. Verikas,et al.  Advances in computational intelligence-based print quality assessment and control in offset colour printing , 2011, Expert Syst. Appl..

[9]  Pierre Baldi,et al.  Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[11]  J. Luo,et al.  Automatic colour printing inspection by image processing , 2003 .

[12]  Lecture Notes in Computational Intelligence and Decision Making - Proceedings of the 2020 International Scientific Conference "Intellectual Systems of Decision-making and Problems of Computational Intelligence", ISDMCI 2020, Kherson, Ukraine, May 25-29, 2020 , 2021, ISDMCI.

[13]  Haoxue Liu,et al.  Color Difference Calculation of Prints for Machine Vision System , 2016 .

[14]  Li Yang,et al.  Advanced Graphic Communications, Packaging Technology and Materials , 2016 .

[15]  Marius Pedersen,et al.  Image quality metrics for the evaluation of printing workflows , 2011 .

[16]  Robert Chung,et al.  A Survey of digital and offset print quality issues , 2007 .