Dr. Sarah John Profile

Dr. Sarah John

Research Associate

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Publications (4)

Proceedings Article | 23 September 2003 Paper

Multiframe information fusion for multispectral imaging through atmospheric turbulence

Sarah John, Mikhail Vorontsov

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.487371

KEYWORDS: Information fusion, Multispectral imaging, Image fusion, Atmospheric turbulence, Error analysis, RGB color model, Image processing, Video, Video processing, Turbulence

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Proceedings Article | 1 March 1991 Paper

Recovery of aliased signals for high-resolution digital imaging

Sarah John

Proceedings Volume 1385, (1991) https://doi.org/10.1117/12.25369

KEYWORDS: Signal to noise ratio, Digital imaging, Imaging devices, Signal processing, Interference (communication), Image resolution, Optical transfer functions, Imaging systems, Image acquisition, Machine vision

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Proceedings Article | 1 October 1990 Paper

Information theoretical assessment of digital imaging systems

Sarah John, Zia-ur Rahman, Friedrich Huck, Stephen Reichenbach

Proceedings Volume 1309, (1990) https://doi.org/10.1117/12.21758

KEYWORDS: Image compression, Systems modeling, Image restoration, Image processing, Signal to noise ratio, Visualization, Image quality, Imaging systems, Analytical research, Thermal modeling

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Proceedings Article | 1 September 1990 Paper

Information theoretical assessment of image gathering and coding for digital restoration

Friedrich Huck, Sarah John, Stephen Reichenbach

Proceedings Volume 1360, (1990) https://doi.org/10.1117/12.24173

KEYWORDS: Image compression, Image processing, Signal to noise ratio, Visualization, Image restoration, Image quality, Computer programming, Visual communications, Information visualization, Quantization

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In this paper we are concerned with the end-to-end performance of image gathering, coding, and restoration as a whole rather than as a chain of independent tasks. Our approach evolves from the pivotal relationship that exists between the spectral information density of the transmitted signal and the restorability of images from this signal. The information theoretical assessment accounts for the information density and efficiency of the acquired signal as a function of the image-gathering system design and the radiance-field statistics, and for the information efficiency and data compression that can be gained by combining image gathering with coding to reduce the signal redundancy and irrelevancy. The redundancy reduction is concerned mostly with the statistical properties of the acquired signal, and the irrelevancy reduction is concerned mostly with the visual properties of the scene and the restored image. The results of this assessment lead to intuitively appealing insights about image gathering and coding for digital restoration. Foremost is the realization that images can be restored with better quality and from less data as the information efficiency of the transmitted data is increased, providing that the restoration correctly accounts for the image gathering and coding processes and effectively suppresses the image-display degradations. High information efficiency, in turn, can be attained only by minimizing imagegathering degradations as well as signal redundancy. Another important realization is that the critical constraints imposed on both image gathering and natural vision limit the maximum acquired information density to ~ 4 binary information units (bifs). This information density requires ~ 5-bit encoding for transmission and recording when lateral inhibition is used to compress the dynamic range of the signal (irrelevancy reduction). This number of encoding levels is close (perhaps fortuitously) to the upper limit of the ~ 40 intensity levels that each nerve fiber can transmit, via pulses, from the retina to the visual cortex within ~l/20 sec to avoid prolonging reaction times. If the data are digitally restored as an image on film for ‘best’ visual quality, then the information density may often be reduced to ~3 bifs or even less, depending on the scene, without incurring perceptual degradations because of the practical limitations that are imposed on the restoration. These limitations are not likely to be found in the nervous system of human beings, so that the higher information density of ^4 bifs that the eye can acquire probably contributes effectively to the improvement in visual quality that we always experience when we view a scene directly rather than through the media of image gathering and restoration.

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