Information Theory Analysis Of Sensor-Array Imaging Systems For Computer Vision

F. O. Huck; C. L. Fales; S. K. Park; R. W. Samms; M. O. Self

doi:10.1117/12.935283

26 October 1983 Information Theory Analysis Of Sensor-Array Imaging Systems For Computer Vision

F. O. Huck, C. L. Fales, S. K. Park, R. W. Samms, M. O. Self

Proceedings Volume 0397, Applications of Digital Image Processing V; (1983) https://doi.org/10.1117/12.935283
Event: 1983 International Technical Conference/Europe, 1983, Geneva, Switzerland

Abstract

Information theory is used to assess the performance of sensor-array imaging systems, with emphasis on the performance obtained with image-plane signal processing. By electronically controlling the spatial response of the imaging system, as suggested by the mechanism of human vision, it is possible to trade-off edge enhancement for sensitivity, increase dynamic range, and reduce data transmission. Computational results show that: signal information density varies little with large variations in the statistical properties of random radiance fields; most information (generally about 85 to 95%) is contained in the signal intensity transitions rather than levels; and performance is optimized when the OTF of the imaging system is nearly limited to the sampling passband to minimize aliasing at the cost of blurring, and the SNR is very high to permit the retrieval of small spatial detail from the extensively blurred signal. Shading the lens aperture transmittance to increase depth of field and using a regular hexagonal sensor-array instead of square lattice to decrease sensitivity to edge orientation also improves the signal information density up to about 30% at high SNRs.

Citation Download Citation

F. O. Huck, C. L. Fales, S. K. Park, R. W. Samms, and M. O. Self "Information Theory Analysis Of Sensor-Array Imaging Systems For Computer Vision", Proc. SPIE 0397, Applications of Digital Image Processing V, (26 October 1983); https://doi.org/10.1117/12.935283

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