A class of robust weighted median (WM) sharpening algorithms is developed in this paper. Unlike traditional linear sharpening methods, weighted median sharpeners are shown to be less sensitive to background random noise or to image artifacts introduced by JPEG and other compression algorithms. These concepts are extended to include data dependent weights under the framework of permutation weighted medians leading to tunable sharpeners that, in essence, are insensitive to noise and compression artifacts. Permutation WM sharpeners are subsequently generalized to smoother/sharpener structures that can sharpen edges and image details while simultaneously filter out background random noise. A statistical analysis of the various algorithms is presented, theoretically validating the characteristics of the proposed sharpening structures. A number of experiments are shown for the sharpening of JPEG compressed images and sharpening of images with background film-grain noise. These algorithms can prove useful in the enhancement of compressed or noisy images posted on the World Wide Web (WWW) as well as in other applications where the underlying images are unavoidably acquired with noise.

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In this paper, we present a new algorithm for aperiodic clustered-dot halftoning based on direct binary search (DBS). The DBS optimization framework has been modified for designing clustered-dot texture, by using filters with different sizes in the initialization and update steps of the algorithm. Following an intuitive explanation of how the clustered-dot texture results from this modified framework, we derive a closed-form cost metric which, when minimized, equivalently generates stochastic clustered-dot texture. An analysis of the cost metric and its influence on the texture quality is presented, which is followed by a modification to the cost metric to reduce computational cost and to make it more suitable for screen design.

Many relational operations are best performed when the relations are stored sorted over the relevant attributes (e.g. the common attributes in a natural join operation). However, generally relations are not stored sorted because it is expensive to maintain them this way (and impossible whenever there is more than one relevant sort key). Still, many times relations turn out to be nearly-sorted, where most tuples are close to their place in the order. This state can result from "leftover sortedness", where originally sorted relations were updated, or were combined into interim results when evaluating a complex query. It can also result from weak correlations between attribute values. Currently, nearly-sorted relations are treated the same as unsorted relations, and when relational operations are evaluated for them, a generic algorithm is used. Yet, many operations can be computed more efficiently by an algorithm that exploits this near-ordering.However, to consistently benefit from using such algorithms the system should also refrain from using the wrong algorithm for relations which happen not to be sorted at all. Thus, an efficient test is required, i.e., a very fast approximation algorithm for establishing whether a given relation is sufficiently nearly-sorted.In this paper, we provide the theoretical foundations for improving query evaluation over possibly nearly-sorted relations. First we formally define what it means for a relation to be nearly-sorted, and show how operations over such relations, such as natural join, set operations and sorting, can be executed significantly more efficiently using an algorithm that we provide. If a relation is nearly-sorted enough, then it can be sorted using two sequential reads of the relation, and writing no intermediate data to disk. We then construct efficient probabilistic tests for approximating the degree of the near-sortedness of a relation without having to read an entire file. The role of our algorithms in a database managePermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ICDT 2010, March 21-24, 2011, Uppsala, Sweden. Copyright 2011 ment system setting is illustrated as soon as the theoretical foundation is laid out.Finally, we outline factors that relate to practical implementations of our algorithms. We show how our test can be enhanced to provide an approximation rather than just a yes-no answer, and discuss its implementability in reallife scenarios where some sparseness may be present in the database files (e.g. if they were created using a B*-tree approach). We also show how our sort can benefit distributed systems and systems that use a solid-state drive, which may very well become prevalent in...

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