By Ludwik Kurz

A key challenge in sensible photograph processing is the detection of particular good points in a loud snapshot. research of variance (ANOVA) recommendations could be very potent in such events, and this booklet offers a close account of using ANOVA in statistical picture processing. The e-book starts via describing the statistical illustration of pictures within the quite a few ANOVA versions. The authors current a few computationally effective algorithms and methods to house such difficulties as line, part, and item detection, in addition to snapshot recovery and enhancement. by way of describing the fundamental ideas of those strategies, and displaying their use in particular occasions, the booklet will facilitate the layout of latest algorithms for specific functions. it will likely be of serious curiosity to graduate scholars and engineers within the box of picture processing and development popularity.

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Pappas and Neuhoff, by employing a dot model into the halftoning algorithm, directly addressed the problems of printer distortion where the printer's ability to print isolated dots reliably (with little variation in the size and/or shape of dots from printed dot to printed dot) affects and sometimes dictates which arrangements of dots, whether dispersed as in FM or clustered as in AM, can or cannot be used. This brings about another very important aspect of halftoning that has surfaced as a direct result of the blue-noise model, that is that even with ten years of technological improvements, blue-noise has made little inroads into electrophotographic printing or digital offset printing.

Some major improvements over the past decade [36]. One of the more profound improvements has been the introduction of threshold modulation where the threshold used to quantize an input pixel to either one or zero is varied in a given fashion. An early approach to threshold modulation was proposed by Ulichney [78] who suggested adding white-noise to the threshold in order to break up worm patterns and periodic textures (Fig. 25). This approach was later shown by Knox [33] to be equivalent to adding low-level white-noise to the original image before halftoning.

At 300dpi and 20in viewing distance, the modeled distortions of Φ1, Φ2, and Φ3 (Fig. 0134 respectively – indicating the superiority of Φ2 for modeling continuous shades of gray. This page intentionally left blank Chapter 3 Blue-Noise Dithering Blue-noise halftoning, Fig. 1, is characterized by a distribution of binary pixels where the minority pixels are spread as homogeneously as possible [79]. Distributing pixels in this manner creates a pattern that is aperiodic, isotropic (radially symmetric) and does not contain any low frequency spectral components.

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