ОЦЕНКА ПОГРЕШНОСТИ ПРИ МНОЖЕСТВЕННОМ СРАВНЕНИИ ЗАШУМЛЕННЫХ ИЗОБРАЖЕНИЙ

Abstract

The aim of this work is to study the effect of noise on the image on the quality of comparison of afinite set of images of the same shape and size. This task inevitably arises when analyzing scenes, detectingindividual objects, detecting symmetry, etc. The noise factor must be taken into account, since thedifference between digital objects can be caused not only by the mismatch of the compared images ofreal objects, but also by distortions due to noise, which is almost always takes place. This differenceturns out to be proportional to the level of the noise component. The main result of this article is ananalytical estimate for the probability of a given level of error, which may arise in the multiple comparisonof a finite set of commensurate digital images. This estimate is based on a low-level comparison,which is a pixel-by-pixel calculation of image differences using the Euclidean metric. In this case, astandard assumption is made about the independent normal noise of image intensities with zero mathematicalexpectation and a priori established standard deviation in each pixel. The evidence presented inthe article allows us to assert that the obtained estimate should be regarded as sufficiently "cautious"and it can be expected that in reality the scatter of the measure caused by noise in the image will besignificantly less than the theoretically found boundary. The estimates obtained in this work are alsouseful for detecting various types of symmetry in images, which, as a rule, lead to the need to calculatethe difference of an arbitrary number of commensurate digital areas. In addition, they can be used astheoretically grounded threshold values in tasks requiring a decision on the coincidence or difference ofimages. Such threshold values inevitably appear at various stages of processing noisy images, and thequestion of their specific values, as a rule, remains open; at best, heuristic considerations are proposedfor their selection.