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A method for measuring digital camera noise by automatic segmentation of a striped target
N.N. Evtikhiev 1, A.V. Kozlov 1, V.V. Krasnov 1, V.G. Rodin 1, R.S. Starikov 1, P.A. Cheremkhin 1

1 National Research Nuclear University MEPhI (Moscow Engineering Physics Institute),
115409, Moscow, Russia, Kashirskoye Shosse 31

 PDF, 2339 kB

DOI: 10.18287/2412-6179-CO-815

Pages: 267-276.

Full text of article: Russian language.

Abstract:
Currently, cameras are widely used in scientific, industrial and amateur tasks. Thus, one needs to be able to quickly evaluate characteristics and capabilities of a particular camera. A method for measuring noise components of the camera photosensor is proposed. It allows one to estimate shot noise, dark temporal noise, photo response non-uniformity and dark signal non-uniformity. For noise measurement, just two images of the same scene need to be registered. The scene consists of several stripes (quasihomogeneous regions). Then the images are processed by automatic signal segmentation. The performance and accuracy of the proposed method are higher than or equal to other fast methods. The experimental results obtained are similar to those derived using a time-consuming standard method within a measurement error.

Keywords:
digital cameras, sensors, digital image processing, image segmentation, shot noise, photo response non-uniformity.

Citation:
Evtikhiev NN, Kozlov AV, Krasnov VV, Rodin VG, Starikov RS, Cheremkhin PA. A method for measuring digital camera noise by automatic segmentation of a striped target. Computer Optics 2021; 45(2): 267-276. DOI: 10.18287/2412-6179-CO-815.

Acknowledgements:
This work was supported by the MEPhI's Competitiveness Growth Program.

References:
  1. Cotton C. The photograph as contemporary art. 3rd ed. New York: Thames & Hudson; 2015.
  2. Thaker AD, Patel SM, Solanki PM. Morphological analysis and mapping of complex craters of 2 Copernican age: Crookes, Ohm and Stevinus. Planet Space Sci 2020; 184: 104856. DOI: 10.1016/j.pss.2020.104856.
  3. Cerrato-Alvarez M, Frutos-Puerto S, Miró-Rodríguez C, Pinilla-Gil E. Measurement of tropospheric ozone by digital image analysis of indigotrisulfonate-impregnated passive sampling pads using a smartphone camera. Microchem J 2020; 154: 104535. DOI: 10.1016/j.microc.2019.104535.
  4. Cai F, Wang T, Lu W, Zhang X. High-resolution mobile bio-microscope with smartphone telephoto camera lens. Optik 2020; 207: 164449. DOI: 10.1016/j.ijleo.2020.164449.
  5. Mai HH, Le TTh. Testing edible oil authenticity by using smartphone based spectrometer. Computer Optics 2020; 44(2): 189-194. DOI: 10.18287/2412-6179-CO-604.
  6. Evtikhiev NN, ZlokazovEYu, Krasnov VV, Rodin VG, Starikov RS, Cheremkhin PA. High-speed implementation of holographic and diffraction elements using digital micromirror devices. Quantum Electron 2020; 50(7): 667-674. DOI: 10.1070/QEL17295.
  7. Grizbil BA, Maksimova LA, Ryabukho VP. Diffraction model of a laser speckle interferometer for measuring micro-displacements of objects with scattering surface. Computer Optics 2020; 44(4): 568-577. DOI: 10.18287/2412-6179-CO-702.
  8. Goncharov DS, Evtikhiev NN, Krasnov VV, Ponomarev NM, Starikov RS. The influence of additional phase modulation of an amplitude liquid crystal STLM on the image recognition characteristics in the invariant optical digital correlator. Computer Optics 2019; 43(2): 200-208. DOI: 10.18287/2412-6179-2019-43-2-200-208.
  9. Evtikhiev NN, Krasnov VV, Kuzmin ID, Molodtsov DYu, Rodin VG, Starikov RS, Cheremkhin PA. QR-code optical encryption in the scheme with spatially incoherent illumination based on two micromirror light modulators. Quantum Electron 2020; 50(2): 195-196. DOI: 10.1070/QEL17139.
  10. European Machine Vision Association, EMVA Standard 1288. Standard for characterization of image sensors and cameras 3.1 2016. Source: <http://www.emva.org/cms/upload/ Standards/>.
  11. Rakhshanfar M, Amer MA. Estimation of Gaussian, Poissonian-Gaussian, and processed visual noise and its level function. IEEE Trans Image Process 2016; 25(9): 4172-4185. DOI: 10.1109/TIP.2016.2588320.
  12. Maggioni M, Katkovnik V, Egiazarian K, Foi A. Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans Image Process 2013; 22(1): 119-133. DOI: 10.1109/TIP.2012.2210725.
  13. Liu C, Szeliski R, Kang SB, Zitnick CL, Freeman WT. Automatic estimation and removal of noise from a single image. IEEE Trans Pattern Anal Mach Intell 2008; 30(2): 299-314. DOI: 10.1109/TPAMI.2007.1176.
  14. Yesilyurt AB, Erol A, Kamisli F, Alatan AA. Single image noise level estimation using dark channel prior. Proceedings of International Conference on Image Processing 2019: 2065-2069. DOI: 10.1109/ICIP.2019.8803150.
  15. Foi A, Trimeche M, Katkovnik V, Egiazarian K. Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data. IEEE Trans Image Process 2008; 17(10): 1737-1754. DOI: 10.1109/TIP.2008.2001399.
  16. Foi A, Alenius S, Katkovnik V, Egiazarian K. Noise measurement for raw-data of digital imaging sensors by automatic segmentation of nonuniform targets. IEEE Sens J 2007; 7(10): 1456-1461. DOI: 10.1109/JSEN.2007.904864.
  17. Cheremkhin PA, Evtikhiev NN, Starikov SN, Krasnov VV, Rodin VG. Modified temporal noise measurement method with automatic segmentation of nonuniform target, its accuracy estimation, and application to cameras of different types. Opt Eng 2014; 53(10): 102107. DOI: 10.1117/1.OE.53.10.102107.
  18. Cheremkhin PA, Evtikhiev NN, Krasnov VV, Rodin VG, Starikov RS, Starikov SN. Fast measurement of temporal noise of digital camera’s photosensors. Proc SPIE 2015; 9648: 96480R. DOI: 10.1117/12.2194979.
  19. Nakamura J. Image sensors and signal processing for digital still cameras. Boca Raton: CRC Press; 2006.
  20. Maître H. From photon to pixel: The digital camera handbook. Hoboken: Wiley; 2017.
  21. Bertalmío M. Denoising of photographic images and video. Fundamentals, open challenges and new trends. Cham, Switzerland: Springer; 2018.
  22. Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994; 81(3): 425-455. DOI: 10.1093/biomet/81.3.425.

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