Edge detection in correlated noise using latin square masks
David Stern, LUDWIK KURZ · Pattern Recognition · 1988
In this paper new classes of algorithms are developed for processing of two-dimensional image data imbedded in correlated noise. The algorithms are based on modifications of standard analysis of variance (ANOVA) techniques involving Latin Square (LS) technique and ensuring their proper operation in dependent noise. The LS technique enables us to analyse three effects, including the gray level (or diagonal) effect instead of two based on the same data, for two-way designs. Though the theoretical development leading to the actual image processing procedure is laborious and complicated, the actual procedure is simple, robust and useful in real-time applications. The efficiency of all algorithms for processing image data corrupted by statistically-dependent noise is verified by extensive Monte-Carlo simulations.