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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Iranian Mathematical Society (IMS)</PublisherName>
				<JournalTitle>Bulletin of the Iranian Mathematical Society</JournalTitle>
				<Issn>1017-060X</Issn>
				<Volume>37</Volume>
				<Issue>No. 2</Issue>
				<PubDate PubStatus="epublish">
					<Year>2011</Year>
					<Month>07</Month>
					<Day>15</Day>
				</PubDate>
			</Journal>
<ArticleTitle>PAIRED ANISOTROPIC DISTRIBUTION FOR IMAGE
SELECTIVE SMOOTHING</ArticleTitle><FirstPage>117</FirstPage>
			<LastPage>131</LastPage>
			<Language>en</Language>
<AuthorList>
<Author>
					<FirstName>A. </FirstName>
					<LastName>MADANKAN</LastName>
					<Affiliation></Affiliation>
				</Author>
</AuthorList>
			<History>
				<PubDate PubStatus="received">
					<Year>2008</Year>
					<Month>08</Month>
					<Day>21</Day>
				</PubDate>
			</History>
		<Abstract><![CDATA[‎In this paper‎, ‎we present a novel approach for image selective smoothing by the evolution of two paired nonlinear‎
‎partial differential equations‎. ‎The distribution coefficient in de-noising equation controls the speed of distribution‎, ‎and is‎
‎determined by the edge-strength function‎. ‎In the previous works‎, ‎the edge-strength function depends on isotropic‎
‎smoothing of the image‎, ‎which results in failing to preserve corners and junctions‎, ‎and may also result in failing to resolve‎
‎small features that are closely grouped together‎. ‎The proposed approach obtains the edge-strength function by solving a‎
‎nonlinear distribution equation governed by the norm of the image gradient‎. ‎This edge-strength function is then introduced‎
‎into a well-studied anisotropic distribution model to yield a regularized distribution coefficient for image smoothing‎. ‎An explicit‎
‎numerical scheme is employed to efficiently solve these two paired equations‎. ‎Compared with the existing methods‎, ‎the‎
‎proposed approach has the advantages of more detailed preservation and implementational simplicity‎. ‎Experimental results‎
‎on the synthesis and real images confirm the validity of the proposed approach‎.]]></Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Computer vision</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">anisotropic distribution</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Image smoothing</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">partial differential
equation</Param>
			</Object>
		</ObjectList>
</Article>
</ArticleSet>