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Proceedings Paper

Fixed Pattern Noise pixel-wise linear correction for crime scene imaging CMOS sensor
Author(s): Jie Yang; David W. Messinger; Roger R. Dube; Emmett J. Ientilucci
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Paper Abstract

Filtered multispectral imaging technique might be a potential method for crime scene documentation and evidence detection due to its abundant spectral information as well as non-contact and non-destructive nature. Low-cost and portable multispectral crime scene imaging device would be highly useful and efficient. The second generation crime scene imaging system uses CMOS imaging sensor to capture spatial scene and bandpass Interference Filters (IFs) to capture spectral information. Unfortunately CMOS sensors suffer from severe spatial non-uniformity compared to CCD sensors and the major cause is Fixed Pattern Noise (FPN). IFs suffer from "blue shift" effect and introduce spatial-spectral correlated errors. Therefore, Fixed Pattern Noise (FPN) correction is critical to enhance crime scene image quality and is also helpful for spatial-spectral noise de-correlation. In this paper, a pixel-wise linear radiance to Digital Count (DC) conversion model is constructed for crime scene imaging CMOS sensor. Pixel-wise conversion gain Gi,j and Dark Signal Non-Uniformity (DSNU) Zi,j are calculated. Also, conversion gain is divided into four components: FPN row component, FPN column component, defects component and effective photo response signal component. Conversion gain is then corrected to average FPN column and row components and defects component so that the sensor conversion gain is uniform. Based on corrected conversion gain and estimated image incident radiance from the reverse of pixel-wise linear radiance to DC model, corrected image spatial uniformity can be enhanced to 7 times as raw image, and the bigger the image DC value within its dynamic range, the better the enhancement.

Paper Details

Date Published: 5 May 2017
PDF: 14 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 1019802 (5 May 2017); doi: 10.1117/12.2262035
Show Author Affiliations
Jie Yang, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)
Roger R. Dube, Rochester Institute of Technology (United States)
Emmett J. Ientilucci, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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