A multiresolution noise-removal algorithm for visual pattern recognition in imaging detectors

Abstract An algorithm to process data from imaging detectors is proposed. It performs the preprocessing phase for pattern-recognition tasks to remove noise from input images based on a computer vision model. As an example, the program was applied to reconstruct the pattern of Cherenkov light distributed on a single circle produced and detected by a RICH device at CERN. Title of program: PRIP_ENHANCE Catalogue Id: ACBJ_v1_0 Nature of problem High multiplicity events, generated by recent physics experiments require high granularity detectors in particular those read out by CCD's. These devices produce unambiguous bidimensional representations of spot-composed events thus involving the use of digital image process- ing techniques for data analysis. Random noise from light reflections and CCD noise arise in such a way to elude traditional noise removal methods making unreliable further event pattern recognition strategies. Versions of this program held in the CPC repository in Mendeley Data ACBJ_v1_0; PRIP_ENHANCE; 10.1016/0010-4655(91)90078-Y This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)