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Noise estimation for multispectral visualization

© 2016 D. S. Sidorchuk, I. A. Konovalenko, S. A. Gladilin, Y. I. Maximov

Institute for Information Transmission Problems RAS 127051 Moscow, Bolshoi Karetny lane, 19

Received 18 Apr 2016

We propose the technique of improving multispectral local contrast preserving visualization in noisy environment. The technique is based on weighting of image bands according to estimations of their noise level. We consider two approaches to weighting coef cients calculation: the min/max autocorrelation factors (MAF) method and the new method that considers each channel as a realization of a parameterized gaussian process. Likelihood maximization gives the parameter values which are used to estimate noise. Validity of the ordering given by both methods is verified using manually marked multispectral dataset. We also show examples of visualization improvement obtained by bands weighting. The dataset used consists of publicly available real multispectral images.

Key words: multispectral images, multispectral visualization, noise estimation, local contrast preserving

Cite: Sidorchuk D. S., Konovalenko I. A., Gladilin S. A., Maximov Y. I. Otsenka shumnosti kanalov v zadache vizualizatsii multispektralnykh izobrazhenii [Noise estimation for multispectral visualization]. Sensornye sistemy [Sensory systems]. 2016. V. 30(4). P. 344-350 (in Russian).

References:

  • Kirilin A. N., Akhmetov R. N., Stratilatov N. P., Baklanov A. I., Fedorov V. M., Novikov M. V. Resurs-P spacecraft // Geomatics. 2010. N. 4. P. 23–26 [in Russian].
  • Sokolov V., Norka Y., Karpenko S., Nikolaev D. On Contrast-Preserving Visualization of Multispectral Images // Proceedings of ISA RAS. 2009. V. 45. P. 183–193 [in Russian].
  • Chukalina M., Nikolaev D., Somogyi A., Schaefer G. Multitechnique data treatment for multi-spectral image visualization // In Proc. 22th ECMS. 2008. P. 234–236. DOI 10.7148/2008–0234.
  • Coleman T. F., Li Y. An interior trust region approach for nonlinear minimization subject to bounds // SIAM J. Optimization. 1996. V 6.2. P. 418–445. DOI 10.1137/0806023.
  • Di Zenzo S. A note on the gradient of multi-image // Comp. Vis., Graphics and Image Proc. 1986. V. 33. P. 116–125.
  • Huang X., Zhang L. A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia City, Northern Italy // Int. J. Remote Sens. 2009. V. 30(12). P. 3205–3221. DOI 10.1080/01431160802559046.
  • Kober V. I., Karnaukhov V. N. Restoration of multispectral images distorted by spatially nonuniform camera motion // J. Communicat. Technol. Electron. 2015. V.60(12). P. 1366– 1371. DOI 10.1134/S1064226915120153.
  • Kuznetsova E., Shvets E., Nikolaev D. Viola-Jones based hybrid framework for real-time object detection in multispectral images // Proc. SPIE9875, Eighth Internat. Conf. on Machine Vision. 2015. 98750N. P. 1–6. DOI 10.1117/12.2228707.
  • Liu C., Freeman W. T., Szeliski R., Kang S. B. Noise estimation from a single image // Comp. Vis. and Pattern Recogn., IEEE Comp. Soc. Conf. 2006. V. 1. P. 901–908. DOI 10.1109/ CVPR.2006.207.
  • Olsen S. I. Estimation of noise in images: an evaluation // CVGIP: Graphical Models and Image Proc. 1993. V. 55(4). P. 319– 323. DOI 10.1006/cgip.1993.1022.
  • Porter W., Enmark H. A system overview of the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) // Proc. SPIE. 1987. P. 114–126. DOI 10.1117/12.942280.
  • Rodarmel C., Shan J.Principal Component Analysis for Hyperspectral Image Classi cation // Surv. Land Inform. Syst. 2002. V. 62(2). P. 115–123.
  • Socolinsky D. A., Wolff L. B. A new visualization paradigm for multispectral imagery and data fusion // Comp. Vis. Pattern Recogn. IEEE Comp. Soc. Conf. 1999. V. 1. P. 324. DOI 10.1109/CVPR.1999.786958.
  • Sokolov V., Nikolaev D., Karpenko S., Schaefer G. On Contrast-Preserving Visualization of Multispectral Datasets // Adv. Vis. Comp.: 6th Intern. Sympos., ISVC. Springer Berlin Heidelberg, 2010. P. 173–180. DOI 10.1007/978-3-642- 17289-2_17.
  • Switzer P., Green A. A. Min/max autocorrelation factors for multivariate spatial imagery // Comp. Science Stat. 1984. P. 13–16.