COMPUTATIONAL IMAGING
Better results should be achieved by a procedure in which the image-gathering system is designed specifically to enhance the performance of the image-restoration algorithm to be used.
—W. T. Cathey, B. R. Frieden, W. T. Rhodes, and C. K. Rushforth [43]
10.1 IMAGING SYSTEMS
Optical sensor design balances performance metrics against system implementation and operation constraints. Interesting performance metrics include angular, spatial, or spectral resolution; depth of field; field of view; zoom capacity; camera volume; sensed data efficiency; spectral or polarization sensitivity; and tomographic fidelity. Constraints include fundamental, practical, and financial limits based on physical, information-theoretic, and data processing issues. Examples include spatial and temporal bandwidth, coherence and statistical properties, system model limitations, and computational complexities.
Because of the complexity of system metrics and constraints and the embrionic state of many design tools, none of the designs or design strategies we discuss in this, or previous, chapters are in any sense optimal. While the pessimist may disdain the ad hoc nature of current digital imaging and spectroscopy design, we find promise in the rapidly evolving design landscape and hope that the student finds frank discussion of design strategies illuminating and suggestive.
We may divide ...
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