An Introduction to Nonlinear Image Processing
From a strict semantic point of view, nonlinear image processing encompasses all image processing that is not based on linear operators; however, from a practical, evolutionary point of view, the name itself is usually associated with the study of nonlinear filters, mainly the deterministic and nondeterministic analysis and design of logic-based operators. This Tutorial Text volume explores logic-based operators with emphasis on representation, design, and statistical optimization of nonlinear filters.
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algorithms antiextensive application Astola binary filters binary image binary morphological binary operation binary setting binary signals components Computer Vision conditional expectation consider constraint corresponding corrupted image defined denoted digital image processing disk distribution function Dougherty dual erosion and dilation estimation Euclidean filtered image finite flat structuring element formulation geometric granulometric ideal image idempotent illustrated in Fig image of Fig increasing filters input image iteration kernel linear filters logical Mathematical Morphology Matheron representation means median-type filters methods Morphological Filters Morphological Image Processing morphological operations multiset noisy image nonlinear filters nonlinear image processing observed image open-close opening and closing order statistic output pixel positive Boolean function Proc properties r-opening random variables realization restoration resulting samples shown in Fig Signal Processing SPIE stack filter structuring pairs structuring-element subset text image theory threshold decomposition translation invariant translation-invariant umbra uncorrupted weighted median filters WMMR
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