Nonuniformity correction of infrared imaging arrays by analog techniques

Details Nonuniformity correction of infrared imaging arrays by analog techniques Nonuniformity correction of infrared imaging arrays by analog techniques

May 1995

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995spie.2474....2m&link_type=abstract

Proc. SPIE Vol. 2474, p. 2-13, Smart Focal Plane Arrays and Focal Plane Array Testing, Marc Wigdor; Mark A. Massie; Eds.

Computer Science

Learning

Scientific paper

Analog methods to deal with the nonuniformity correction problem for infrared focal plane arrays have been addressed. On-chip neural-network based techniques and simulation results of these schemes are presented. The approach is based on one in which the nonuniformity correction of individual pixels in accomplished by the method of steepest descent and its well known special case, the least-mean-square method (LMS). Alternative learning methods can be derived from the LMS method by considering only sign of the data or sign of the error. These nonlinear versions of the LMS methods have potential hardware implementation benefits such as power dissipation and simplicity. The simulation results of the nonlinear learning algorithm along with the LMS algorithm results are presented. Analog circuit elements implementing these learning algorithms are also presented. Our simulation results are very useful in that they model the nonidealities that are associated with specific hardware circuit implementation. These include the nonlinearities, scalar multiplicative error and offsets of both feedback multipliers and the integrators. The nonideal factors that cause instability and performance degradation in learning are multiplier and integrator offsets. These factors are shown to be much more important in the pixel offset correction than in the gain correction.

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