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Performance evaluation of ideal low light level imaging system based on the MRC model

  • Jing Sui*
  • , Weiqi Jin
  • , Jianyong Zhang
  • , Yan Zhou
  • *Corresponding author for this work
  • Beijing Institute of Technology

Research output: Contribution to journalConference articlepeer-review

Abstract

The performance of direct viewing low light level (LLL) imaging system is mainly determined by three factors: photons noise, MTF of optical system(OS) and human eyes characteristic. And the image detecting theory which denotes the optimal performance of imaging system has been a positive impetus for the development of the LLL imaging and night vision technique. The system minimum resolvable angle was traditionally used to estimate the image detecting performance which is mainly determined by photons noise at low target illuminance and by MTF at high target illuminance. This criterion can represent the system performance on the whole; however, assuming the signal to noise ratio (SNR)of the image and MTF of OS uncorrelative, is theoretically not complete, since the two factors interrelate actually. From the viewpoint of signal response, the MRC (minimum resolvable contrast) model of the ideal direct viewing LLL imaging system was deduced on the basis of human eyes characteristic. It is a more comprehensive evaluation method for imaging system performance, and can combine with the forecasting model of operating distance to analyze the general performance of night vision system. In conclusion, the relationship and the difference between the MRC model and the traditional detecting equation were investigated.

Original languageEnglish
Article number39
Pages (from-to)189-194
Number of pages6
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5637
DOIs
Publication statusPublished - 2005
EventElectronic Imaging and Multimedia Technology IV - Beijing, China
Duration: 8 Nov 200411 Nov 2004

Keywords

  • Human eyes
  • Low light level imaging system
  • MRC(minimum resolvable contrast)
  • Performance evaluation

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