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A Fixed-Pattern Noise Correction Method Based on Gray Value Compensation for TDI CMOS Image Sensor.

Zhenwang Liu1, Jiangtao Xu2, Xinlei Wang3

  • 1School of Electronic Information Engineering, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China. liuzhenwang@tju.edu.cn.

Sensors (Basel, Switzerland)
|September 22, 2015
PubMed
Summary
This summary is machine-generated.

A novel gray value compensation method effectively eliminates fixed-pattern noise (FPN) in time-delay-integration CMOS image sensors (TDI-CIS). This technique significantly reduces noise in both uniform and real-world images captured by TDI-CIS sensors.

Keywords:
CMOS image sensor (CIS)correction methodfixed-pattern noise (FPN)gray value compensationtime-delay-integration (TDI)

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Area of Science:

  • Image Sensor Technology
  • Signal Processing
  • Optoelectronics

Background:

  • Fixed-pattern noise (FPN) degrades image quality in time-delay-integration CMOS image sensors (TDI-CIS).
  • Existing FPN correction methods may not fully address row and column noise components.
  • Accurate noise estimation is crucial for effective image restoration in TDI-CIS.

Purpose of the Study:

  • To propose and validate a gray value compensation method for eliminating FPN in TDI-CIS.
  • To reduce both row FPN (RFPN) and column FPN (CFPN) in TDI-CIS images.
  • To demonstrate the effectiveness of the proposed method on a 128-stage TDI-CIS sensor.

Main Methods:

  • Capturing 100 images under uniform illumination for noise estimation.
  • Estimating RFPN and CFPN using row-mean and column-mean vectors.
  • Correcting RFPN by adding estimated values and CFPN by subtracting estimated values.

Main Results:

  • The standard deviation of the row-mean vector decreased from 5.6798 to 0.4214 LSB.
  • The standard deviation of the column-mean vector decreased from 15.2080 to 13.4623 LSB.
  • Effective elimination of both RFPN and CFPN in real TDI-CIS images was demonstrated.

Conclusions:

  • The proposed gray value compensation method is highly effective in reducing FPN in TDI-CIS.
  • The method significantly improves image quality by minimizing noise.
  • This approach offers a practical solution for enhancing TDI-CIS performance in various imaging applications.