Fast Primary Image Processing Algorithms in On-Board Vision Systems
Abstract
Multispectral on-board vision systems of aircraft solve a large number of tasks designed to ensure safe flight in difficult visibility conditions and successful flight performance. These tasks are traditionally divided into low-level and high-level tasks. The low-level tasks – or the primary image processing tasks – deal with the topics of noise reduction in processed images, image enhancements and brightness range detection. Problem-solving methods in on-board computers are subject to severe time restrictions in terms of machine time spent for their implementation. Therefore, the entire set of tasks of both low and high level must be solved in real time. This article presents original algorithms for solving two low-level tasks: discrete Gaussian noise reduction and the edge detection. A modified version of the sigma filter, enhanced by an original algorithm of low computational complexity, was applied to estimate noise level in the processed image and to calculate the optimal cut-off value in the sigma filter. A method, that is, on one hand, analogous to the Canny edge detection algorithm, and, on the other, an alternative one. The differences from the Canny edge detection technique are, firstly, in the use of a vector mask to calculate the estimates of private derivatives in the gradient. This mask provides optimal estimates of private derivatives, in terms of the Least Square Method. Formation of smoothed estimator of private derivatives allowed us to refuse from preliminary smoothing of the processed image in conditions of low intensity noise. Secondly, the proposed method uses a different way of forming edges that provide a choice of "strong" and "weak" lines. Unlike the Canny algorithm, the method used to form and use edges is focused on creating a contour image with a minimum number of short contour lines. Short lines make it difficult to analyze the contour image at the stage of solving high-level tasks. The new edge detection method requires 2-3 times less machine time than the Canny edge detection algorithm.
References
[2] Aviacionnye sistemy uluchshennogo i sintezirovannogo videnija: analiticheskij obzor po materialam zarubezhnyh informacionnyh istochnikov [Aviation systems of improved and synthesized vision: Analytical review based on the materials of foreign information sources]. ed. by Fedosov E.A., comp. Vizilter Yu.V. et al. GosNIIAS, Moscow; 2011. (In Russ.)
[3] Kumar N., Kashyap S., Naidu V., Gopalratnam G. Integrated Enhanced and Synthetic Vision System for Transport Aircraft. Defence Science Journal. 2013; 63(2):157-163. (In Eng.) DOI: https://doi.org/10.14429/dsj.63.4258
[4] Furman Ya.Ya., Krevetskiy A.V., Peredreev A.K., Rozhentsov A.A., Khafazov R.G., Egoshina I.L., Leukhin A.N. Vvedenie v konturnyj analiz; prilozhenija k obrabotke izobrazhenij i signalov [Introduction to contour analysis; applications for image and signal processing]. Fizmatlit, Moscow; 2003. (In Russ.)
[5] Loginov A.A., Novikov A.I., Sablina V.A., Shcherbakova O.V. Complex contour analysis feasibility study of contour classifiation and superimposition problems. Vestnik of Ryazan State Radioengineering University. 2013; (43):20-24. Available at: https://www.elibrary.ru/item.asp?id=18886604 (accessed 04.08.2020). (In Russ., abstract in Eng.)
[6] Novikov A.I., Sablina V.A., Nikiforov M.B., Loginov A.A. The contour analysis and image-superimposition problem in computer vision systems. Pattern Recognition and Image Analysis. 2015; 25(1):73-80. (In Eng.) DOI: https://doi.org/10.1134/S1054661815010149
[7] Efimov A.I., Novikov A.I. An algorithm for multistage projective transformation adjustment for image superimposition. Computer Optics. 2016; 40(2):258-266. (In Eng.) DOI: https://doi.org/10.18287/2412-6179-2016-40-2-258-265
[8] Tomasi C., Manduchi R. Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271). Bombay, India; 1998. p. 839-846. (In Eng.) DOI: https://doi.org/10.1109/ICCV.1998.710815
[9] Yang Q., Tan K., Ahuja N. Real-time O(1) bilateral filtering. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA; 2009. p. 557-564. (In Eng.) DOI: https://doi.org/10.1109/CVPR.2009.5206542
[10] Canny J. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986; PAMI-8(6):679-698. (In Eng.) DOI: https://doi.org/10.1109/TPAMI.1986.4767851
[11] Gribkov I.V., Zakharov A.V., Koltsov P.P., Kotovich N.V., Kravchenko A.A., Kutsaev A.S., Osipov A.S. On some issues of the quantitative performance evaluation of edge detectors. Programmnye produkty i sistemy = Software & Systems. 2011; (4):13-19. Available at: https://www.elibrary.ru/item.asp?id=17679097 (accessed 04.08.2020). (In Russ., abstract in Eng.)
[12] Zakharov A.V., Koltsov P.P., Kotovich N.V., Kravchenko A.A., Kutsaev A.S., Osipov A.S. On some methods of comparative study of edge detectors. Trudy NIISI RAN = Proceedings of SRISA RAS. 2012; 2(1):4-13. Available at: https://www.elibrary.ru/item.asp?id=22964684 (accessed 04.08.2020). (In Russ., abstract in Eng.)
[13] Koltsov P.P., Osipov A.S., Kutsaev A.S., Kravchenko A.A., Kotovich N.V., Zakharov A.V. On the quantitative performance evaluation of image analysis algorithms. Computer Optics. 2015; 39(4):542-556. (In Eng.) DOI: https://doi.org/10.18287/0134-2452-2015-39-4-542-556
[14] Maini R., Aggarwal H. Study and Comparison of Various Image Edge Detection Techniques. International Journal of Image Processing. 2009; 3(1):1-11. Available at: https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJIP-15 (accessed 04.08.2020). (In Eng.)
[15] Gonzalez R.C., Woods R.E. Digital Image Processing. 3rd Edition. Pearson; 2007. (In Eng.)
[16] Novikov A.I., Pronkin A.V. Detector of gradient type borders for understanding surface images. Bulletin of the Ryazan State Radio Engineering University. 2019; (68):68-76. (In Russ., abstract in Eng.) DOI: https://doi.org/10.21667/1995-4565-2019-68-2-68-76
[17] Donoho D.L. De-noising by soft-thresholding. IEEE Transactions on Information Theory. 1995; 41(3):613-627. (In Eng.) DOI: https://doi.org/10.1109/18.382009
[18] Olsen S.I. Noise variance estimation in images. In: Proceedings of the 8th Scandinavian Conference on Image Analysis. Troms, Norway; 1993. (In Eng.)
[19] Kumar R., Kumar N., Jung K.H. Color image steganography scheme using gray invariant in AMBTC compression domain. Multidimensional Systems and Signal Processing. 2020; 31(3):1145-1162. (In Eng.) DOI: https://doi.org/10.1007/s11045-020-00701-8
[20] Kovalevsky V. Modern Algorithms for Image Processing: Computer Imagery by Example Using C#. Apress, Berkeley, CA; 2019. (In Eng.) DOI: https://doi.org/10.1007/978-1-4842-4237-7
[21] Amer A., Dubois E. Fast and reliable structure-oriented video noise estimation. IEEE Transactions on Circuits and Systems for Video Technology. 2005; 15(1):113-118. (In Eng.) DOI: https://doi.org/10.1109/TCSVT.2004.837017
[22] Ghazal M., Amer A., Ghrayeb A. Structure-Oriented Spatio-Temporal Video Noise Estimation. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings. Toulouse, France; 2006. p. 845-848. (In Eng.) DOI: https://doi.org/10.1109/ICASSP.2006.1660475
[23] Lapshenkov E.M. No Reference Estimation of Noise Level of Digital Image is Based on Harmonic Analysis. Computer Optics. 2012; 36(3):439-447. Available at: https://www.elibrary.ru/item.asp?id=17962892 (accessed 04.08.2020). (In Russ., abstract in Eng.)
[24] Novikov A.I., Ustyukov D.I. Interference suppressor operators researching in a discrete white noise. Dynamics of Complex Systems – XXI century. 2017; 11(1):26-32. Available at: https://www.elibrary.ru/item.asp?id=29808486 (accessed 04.08.2020). (In Russ., abstract in Eng.)
[25] Kendall M.G., Stuart A. The Advanced Theory of Statistics. Vol. 3: Design and Analysis, and Time Series. London: Charles Griffin & Co., Ltd; 1966. (In Eng.)
[26] Novikov A.I. The Formation of Operators with Given Properties to solve Original Image Processing Tasks. Pattern Recognition and Image Analysis. 2015; 25(2):230-236. (In Eng.) DOI: https://doi.org/10.1134/S1054661815020194
[27] Tikhonov A.N., Arsenin V.Ya. Metody reshenija nekorrektnyh zadach [Methods for the Solution of Ill-Posed Problems]. Nauka, Moscow; 1986. (In Russ.)
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