УДК: 004.932.4

Iteration algorithms for interchannel gradient reconstruction of multicomponent images distorted by Z-axis noise

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Самойлин Е.А., Шипко В.В. Итерационные алгоритмы межканальной градиентной реконструкции многокомпонентных изображений, искаженных аппликативными помехами // Оптический журнал. 2014. Т. 81. № 4. С. 54–60.

Samoylin E.A., Shipko V.V. Iteration algorithms for interchannel gradient reconstruction of multicomponent images distorted by Z-axis noise [in Russian] // Opticheskii Zhurnal. 2014. V. 81. № 4. P. 54–60.

For citation (Journal of Optical Technology):

E. A. Samoĭlin and V. V. Shipko, "Iteration algorithms for interchannel gradient reconstruction of multicomponent images distorted by Z-axis noise," Journal of Optical Technology. 81(4), 209-214 (2014). https://doi.org/10.1364/JOT.81.000209

Abstract:

This paper presents iteration algorithms for the interchannel gradient reconstruction of the signals of multicomponent digital images distorted by Z-axis noise. The results of numerical studies show that the distorted elements of multicomponent images are reconstructed more accurately when the proposed algorithms are used than they are when known algorithms are used.

Keywords:

multicomponent images, Z-axis noise, interchannel gradient reconstruction, median filtering

OCIS codes: 100.2000

References:

1. V. V. Eremeev, A. A. Makarenkov, A. É. Moskvitin, and A. A. Yudakov, “Increasing the distinctness of with which objects are depicted on the data of a hyperspectral recording of the earth’s surface,” Tsif. Obrab. Sig. No. 3, 35 (2012).
2. P. V. Kalinin and A. A. Sirota, “Modeling Z-axis distortions with various degrees of transparency and random shape,” Tsif. Obrab. Sig. No. 1, 28 (2013).
3. Yu. L. Koziratskiı˘ and P. M. Yukhno, “Synthesizing optical noise,” Radiotekhnika No. 10, 52 (2000).
4. Yu. G. Yakushenkov, V. N. Lukantsev, and M. P. Kolosov, Methods of Combatting Noise in Optoelectronic Devices (Radio i Svyaz’, Moscow, 1981).

5. E. A. Samoı˘lin and V. V. Shipko, “Method of interchannel compensation of pulse noise in problems of the restoration of multicomponent digital images,” Opt. Zh. 80, No. 10, 53 (2013) [J. Opt. Technol. 80, 623 (2013)].
6. E. A. Samoı˘lin and V. V. Shipko, “Method of interchannel gradient reconstruction of distorted signals of digital color images,” Tsif. Obrab. Sig. No. 3, 13 (2013).
7. E. A. Samoı˘lin, “Algorithms for reconstructing digital optical images distorted by pulse noise,” Opt. Zh. 74, No. 9, 50 (2007) [J. Opt. Technol. 74, 617 (2007)].
8. E. A. Samoı˘lin, “Algorithms for estimating the pulse noise in problems of digital filtering of optical images,” Opt. Zh. 73, No. 12, 42 (2006) [J. Opt. Technol. 73, 859 (2006)].
9. E. A. Samoı˘lin, V. V. Shipko, and P. A. Trifonov, “Iteration algorithm for reconstructing digital images with adaptive detection of pulse noise,” in Materials of the Nineteenth International Scientific Conference on Radar, Navigation, and Communications, Vol. 1 (Izd. NPF SAKVOEE OOO, Voronezh, 2013), pp. 182–189.
10. Yu. E. Voskoboı˘nikov and V. G. Belyavtsev, “Nonlinear algorithms for filtering vector signals,” Avtometriya No. 5, 97 (1999).
11. E. A. Samoı˘lin, “Criteria for evaluating the quality of the filtering of pulse noise on images,” Avtometriya 42, No. 4, 25 (2006).
12. V. Lukin, “Processing of multichannel RS data for environment monitoring,” in Proceedings of NATO Advanced Research Workshop on Geographical Information Processing and Visual Analytics for Environmental Security, Trento, Italy, July 2009, pp. 129–138.