S. S. Magazov Image Recovery on Defective Pixels of a CMOS and CCD Arrays
S. S. Magazov Image Recovery on Defective Pixels of a CMOS and CCD Arrays


The article investigates the problem of restoring a raster image on defective areas of CCD or CMOS matrices. The task of image restoration is divided into subtasks: restoration of contours and texture restoration. These problems are solved by a special image recovery machine, which uses image classification methods adapted to the task, a neural network, and image recovery methods. An original method of image restoration in the video series is proposed. The analysis of the computational complexity of the methods is fulfilled.


texture generation, discrete function approximation, image statistics, Haralik and Laws parameters, Gabor filter, neural networks.

PP. 25-40.

DOI 10.14357/20718632190303


1. Alfred V. Aho, Jeffrey D. Ullman. The Theory of Parsing, Translation, and Compiling, Volume 1: Parsing, 1972
2. Furman Ya.A. Krevetsky A.V. Peredreev A.K. Rozhentsov A.A. Khafizov R.G. Egoshina I.L. Leukhin A.N. Introduction to contour analysis. - M.: FIZMATLIT, 2005. –561 p.
3. O. Acuna, Texture modeling using Gibbs distributions, Computer Vision, Graphics, and Jmage Processing: Graphical Models and Jmage Processing. 54(5), 210{222, (1992). 50.
4. Barnsely M. F. The science of fractal images – Springer-Verlag, NJ, 1988. – JSBN:0-587-96608-0
5. Cross G. C., Jain A. K. Markov random end texture models, JEEE Transactions on Pattern Analysis and Machine intelligence. 5, (1985).
6. Chellappa R., Chatterjee S., Classification of textures using Gaussian Markov random fields, JEEE Transactions on Acoustics, Speech, and Signal Processing. ASSP{55(4), 959{965, (1985).
7. Fu K.S., Syntactic Pattern Recognition and Applications, Prentice-Hall, New Jersej, 1982.
8. Falconer K. J. Fractal geometry mathematical foundations and applications 2nd Ed. // JEEE, 2005. – 566 p. – JSBN 978-047084861677
9. Gabor D., Wilby W. P. and Woodcock R. (1960) A universal nonlinear filter, predictor and simulator which optimises itself by a learning process. IEE Proc., 108: 422–438.
10. Goodfellow I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, 2014.
11. Haralick R. M. Statistical and structural approaches to teiture // Proceedings of the JEEE, 1979. – Vol 67, 5. – P. 786-804.
12. Haralick R. M. Textural Features for image classification //JEEE Transactions on systems, man and cybernetics, volume SMC-5. – JEEE, 1979. – 6. – P. 610-621. JSSN 0018- 9472.
13. Harte D. Multifractals. Theory and applications // Chapman & Hall/CRC, 2001. – 264 p. – JSBN 1-58488-154-2
14. Laws K.I. Textured Image Segmentation. PhD thesis, University of Southern California, LA, 1980.
15. Ljn Ljang, Ce Lju, Jng-Qjng Ju ect. Real-Time Texture Synthesis bj Patch-Based Sampling Microsoft Research China, Beijing
16. O. L. Vovk. Evaluation of statistical features for texture classification // The Visnyk of the SSU, 2004. Vol. 71, no. 12, p. 98–105.
17. Yong Xu, Hui Ji, Cornelia Fermuller. Viewpoint Invariant Texture Description Using Fractal Analysis. Int. J. Comp. Vis, 2009, 83, pp. 85-100.

2019 / 03
2019 / 02
2019 / 01
2018 / 04

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".