Accurate estimation of camera shot noise in the real-time

Nowadays digital cameras are essential parts of various technological processes and daily tasks. They are widely used in optics and photonics, astronomy, biology and other various fields of science and technology such as control systems and video-surveillance monitoring. One of the main information limitations of photo- and videocameras are noises of photosensor pixels. Camera’s photosensor noise can be divided into random and pattern components. Temporal noise includes random noise component while spatial noise includes pattern noise component. Temporal noise can be divided into signal-dependent shot noise and signal-nondependent dark temporal noise. For measurement of camera noise characteristics, the most widely used methods are standards (for example, EMVA Standard 1288). It allows precise shot and dark temporal noise measurement but difficult in implementation and time-consuming. Earlier we proposed method for measurement of temporal noise of photo- and videocameras. It is based on the automatic segmentation of nonuniform targets (ASNT). Only two frames are sufficient for noise measurement with the modified method. In this paper, we registered frames and estimated shot and dark temporal noises of cameras consistently in the real-time. The modified ASNT method is used. Estimation was performed for the cameras: consumer photocamera Canon EOS 400D (CMOS, 10.1 MP, 12 bit ADC), scientific camera MegaPlus II ES11000 (CCD, 10.7 MP, 12 bit ADC), industrial camera PixeLink PL-B781F (CMOS, 6.6 MP, 10 bit ADC) and video-surveillance camera Watec LCL-902C (CCD, 0.47 MP, external 8 bit ADC). Experimental dependencies of temporal noise on signal value are in good agreement with fitted curves based on a Poisson distribution excluding areas near saturation. Time of registering and processing of frames used for temporal noise estimation was measured. Using standard computer, frames were registered and processed during a fraction of second to several seconds only. Also the accuracy of the obtained temporal noise values was estimated.

[1]  Jens H. Krüger,et al.  A Survey of General‐Purpose Computation on Graphics Hardware , 2007, Eurographics.

[2]  Increase in the Sensitivity and Selectivity of Semiconductor Gas Sensors , 2015 .

[3]  Sen M. Kuo,et al.  Active noise control: a tutorial review , 1999, Proc. IEEE.

[4]  J. Homola.  Surface plasmon resonance sensors for detection of chemical and biological species. , 2008, Chemical reviews.

[5]  Robert W. Boyd,et al.  Imaging with a small number of photons , 2014, Nature Communications.

[6]  A. V. Bondarenko,et al.  Counterfeit bill detection by image analysis for smartphones Oпpeдeлeнie пoдлiннocTi бaнkнoT нa ocнoвe aнaлiзa iзoбpaжeнiй длya cnapTфoнa , 2017 .

[7]  Martin Stevens,et al.  Image calibration and analysis toolbox – a free software suite for objectively measuring reflectance, colour and pattern , 2015, Methods in ecology and evolution.

[8]  I. M'arquez,et al.  X-ray spectral variability of seven LINER nuclei with XMM-Newton and Chandra data , 2013, 1305.2225.

[9]  T. Fromherz,et al.  CMOS-compatible graphene photodetector covering all optical communication bands , 2013, 1302.3854.

[10]  Ming-Ju Huang,et al.  Two-step phase shifting differential-recording digital holographic microscopy , 2017, Scientific Reports.

[11]  Félix Salazar Bloise,et al.  Non-Contact Surface Roughness Measurement by Implementation of a Spatial Light Modulator , 2017, Sensors.

[12]  Realisation of invariant holographic filters with the linear phase coefficient in a Van der Lugt correlator , 2008 .

[13]  Aloka Sinha,et al.  Securing information using optically generated biometric keys , 2016 .

[14]  Elmar Pfeffermann,et al.  The European Photon Imaging Camera on XMM-Newton: The pn-CCD camera , 2001 .

[15]  Roland Siegwart,et al.  Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments , 2014, IEEE Robotics & Automation Magazine.

[16]  D. Monet,et al.  THE FOURTH US NAVAL OBSERVATORY CCD ASTROGRAPH CATALOG (UCAC4) , 2012, 1212.6182.

[17]  Ram Gopal Raj,et al.  Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network , 2017, Sensors.

[19]  N. Petrov,et al.  Correlation Characterization of Particles in Volume Based on Peak-to-Basement Ratio , 2017, Scientific Reports.

[20]  Wen-mei W. Hwu,et al.  Optimization principles and application performance evaluation of a multithreaded GPU using CUDA , 2008, PPoPP.

[21]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[22]  Kevin Skadron,et al.  Scalable parallel programming , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).

[23]  Eric Monmasson,et al.  FPGA Design Methodology for Industrial Control Systems—A Review , 2007, IEEE Transactions on Industrial Electronics.

[24]  Patrick S Doyle,et al.  Universal process-inert encoding architecture for polymer microparticles. , 2014, Nature materials.

[25]  Dietmar Stalke,et al.  Comparison of silver and molybdenum microfocus X-ray sources for single-crystal structure determination , 2015, Journal of applied crystallography.

[26]  Pavel A. Cheremkhin,et al.  Improvement of quality of optical reconstruction of digital Fourier holograms displayed on phase-only SLM by its digital preprocessing , 2014, Security and Defence.

[27]  Nikolay N. Evtikhiev,et al.  Projection multiplex recording of computer-synthesised one-dimensional Fourier holograms for holographic memory systems: mathematical and experimental modelling , 2015 .

[28]  Wiendelt Steenbergen,et al.  Laser speckle contrast imaging: theoretical and practical limitations , 2013, Journal of biomedical optics.

[29]  L. C. Cui,et al.  Characterization of digital image noise properties based on RAW data , 2006 .

[30]  Nikolay N. Evtikhiev,et al.  Measurement of noises and modulation transfer function of cameras used in optical-digital correlators , 2011, Electronic Imaging.

[31]  K. Egiazarian,et al.  Noise Measurement for Raw-Data of Digital Imaging Sensors by Automatic Segmentation of Nonuniform Targets , 2007, IEEE Sensors Journal.

[32]  Scott Hauck,et al.  Reconfigurable computing: a survey of systems and software , 2002, CSUR.

[33]  Donald B. Hondongwa,et al.  A Review of the Pinned Photodiode for CCD and CMOS Image Sensors , 2014, IEEE Journal of the Electron Devices Society.

[34]  John D. Owens,et al.  GPU Computing , 2008, Proceedings of the IEEE.

[35]  J. Lillo-Box,et al.  High-resolution imaging of Kepler planet host candidates - A comprehensive comparison of different techniques , 2014, 1405.3120.

[36]  Junichi Nakamura,et al.  Image Sensors and Signal Processing for Digital Still Cameras , 2005 .

[37]  K. Abazajian,et al.  THE SEVENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY , 2008, 0812.0649.

[38]  William J. Dally,et al.  The GPU Computing Era , 2010, IEEE Micro.

[39]  Pinhas Girshovitz,et al.  Doubling the field of view in off-axis low-coherence interferometric imaging , 2014, Light: Science & Applications.

[40]  Pavel A. Cheremkhin,et al.  Fast measurement of temporal noise of digital camera's photosensors , 2015, SPIE Security + Defence.

[41]  A. Fercher,et al.  Optical coherence tomography - principles and applications , 2003 .

[42]  Pavel A. Cheremkhin,et al.  Modified temporal noise measurement method with automatic segmentation of nonuniform target, its accuracy estimation, and application to cameras of different types , 2014 .