Adaptive Calibration of Imaging Array Detectors

Abstract

A A A Ab b b bs s s st t t tr r r ra a a ac c c ct t t t: In this paper we present two methods for non-uniformity correction of imaging array detectors based on neural networks, both of them exploit image properties to supply lack of calibrations and maximize the entropy of the output. The first method uses a self-organizing net that produces a linear correction of the raw data with coefficients that adapt continuously. The second method employs a kind of contrast equalization curve to match pixel distributions. Our work originates from Silicon detectors but the treatment is general enough to be applicable to many kinds of array detectors like those used in Infrared imaging or in high energy physics. Purpose of this paper is to show that itÕs possible to calculate pixel parameters without explicit calibrations substituting the lacking information with some hypotheses on incoming data. A simple example is this: if one knows that pixel A always receives the same flux of pixel B, then, after a few images, one can get the relative calibration of A and B: the knowledge that A and B must see the same partly replaces information that would come from a calibration.We will present two different methods, based on two different sets of hypotheses. Our first method make the hypothesis that images arriving on the detector follow a Gibbs 1 mbh@trieste.infn.it, http://www.ts.infn.it/~mbh/MBHgeneral.html, renato@mediastudio.it