Fusion algorithm of a sequence of different-range images for devices with electrically controlled lens
S.M. Borzov, A.V. Golitsyn, O.I. Potaturkin

 

 Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia,
Novosibirsk Branch of the Institute of Semiconductor Physics, Novosibirsk, Russia,
Siberian Branch of the Russian Academy of Sciences,
«Technological Design Institute of Applied Microelectronics», Novosibirsk, Russia,

Novosibirsk State University, Novosibirsk, Russia

Full text of article: Russian language.

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Abstract:
Methods for generating a synthesized image with increased depth of field from a sequence of real-time frames received by varying-range focusing using liquid optics are investigated. An algorithm based on constructing a map of ranges by analyzing a contrast pyramid and the weighed pixel-by-pixel summation of the initial images whose coefficients are proportional to the number of same-range pixels in each local neighborhood is developed.

Keywords:
electrically controlled lens, liquid lens, increased depth of field, fusion algorithm, image reconstruction-restoration, image processing.

Citation:
Borzov SM, Golitsyn AV, Potaturkin OI. Fusion algorithm of a sequence of different-range images for devices with electrically controlled lens. Computer Optics 2016; 40(3): 388-394. DOI: 10.18287/2412-6179-2016-40-3-416-421.

References:

  1. Soifer VA, Kotlyar VV, Kazanskiy NL, Doskolovich LL, Kharitonov SI, Khonina SN, Pavelyev VS, Skidanov RV, Volkov AV, Golovashkin DL, Solovyev VS, Usplenyev GV. Methods for computer design of diffractive optical elements. Ed by Soifer VA. New York: John Wiley & Sons, Inc; 2002.
  2. Borzov SM, Uzilov SB. Development of multiframe noise reduction algorithm for mobile thermalvision systems [In Russian]. Novosibirsk State University Journal of Information Technologies. 2013; 11(1): 16-23.
  3. Kirichuk VS, Kosykh VP, Popov SA, Sinel’shchikov VV. Suppression of a quasi-stationary background in a sequence of images by means of interframe processing. Optoelectronics, Instrumentation And Data Processing 2014; 50(2): 109-117.
  4. Golitsyn АV, Еfremov VS, Мikhailov IО, Orevkova NV, Fedorov BV, Shlishevcky VB. Liquid lenses as the new optical element base for optical and optoelectronic devices [In Russian]. Interekspo GEO -Siberia-2013: IX international exhibition and scientific congress, 15-26 apr. 2013, Novosibirsk; "SibOptika-2013". Novosibirsk: SSUGT. 2013. 1: 7-11.
  5. Zhang DY, Lien V, Berdichevsky Y, Choi J, Lo Y-H. Adaptive Lens with High Focal Length Tunability. Applied Physics Letters 2003; 82: 3171-3172.
  6. Efremov VS, Shlishevskiy VB. Optical materials and achromatic correction of typical components of optical systems [In Russian]. Novosibirsk: SSUGT; 2013.
  7. Golitsyn АV. Electro controlled wide spectral objective with liquid lenses [In Russian]. The Russian conference on actual problems of semi-conductor photoelectronics «Fotonika-2015» (Novosibirsk, 12-16 оct 2015): 90.
  8. Aksenov OYu. Overlapping of images [In Russian]. Digital signal processing 2005; 3: 51-5.
  9. Vasil’ev AS, Korotaev VV, Krasnyashhix AV, Lashmakov OYu, Nenarokomov ON. Overlapping thermal and television images at inspection of building designs of buildings and constructions [In Russian]. Journal of Instrument Engineering 2012; 55(4): 12-16.
  10. Yoo S, Jo S, Choi K, Jeong C. A Framework for Multisensor Image Fusion using Graphics Hardware. Proceedings of the 11-th International Conference on Information Fusion, June, 2008: 1-5.
  11. Anshakov GP, Rashchupkin AV, Zhuravel YN. Нyperspectral and multispectral “Resurs-P” data fusion for increase of their informational content. Computer Optics 2015; 39(1): 77-82.
  12. Potaturkin OI, Borzov SM, Potaturkin АО, Uzilov SB. Methods and technologies of processing multi and the hyperspectral data for the high resolution remote sensing [In Russian]. Computational Technologies 2013; 18: 53-60.
  13. Zhuravel IM. Short course of theory of image processing [In Russian]. Moscow; 1999.
  14. Zheng Y, Essock EA, Hansen BC, Haun AM. A new metric based on extended spatial frequency and its application to DWT based fusion algorithms. Information Fusion 2007; 8(2): 177-192. DOI: 10.1016/j.inffus.2005.04.003.
  15. Zheng Y, Essock EA, Hansen BC. An advanced image fusion algorithm based on wavelet transform incorporation with PCA and morphological processing. Proc SPIE 2004; 5298: 177-187. DOI: 10.1117/12.523966.
  16. Burt PJ, Adelson EH. The Laplacian pyramid as a compact image code. IEEE Transactions on Communications 1983; 31(4): 532-540. DOI: 10.1109/TCOM.1983.1095851.
  17. Smith MI, Heather JP. Review of image fusion technology in 2005. Proc SPIE 2005; 5782: 29-45.
  18. Blum RS, Liu Z, eds. Multi-Sensor Image Fusion and Its Applications. Taylor & Francis, CRC Press, 2005.

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