<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462017000200381</article-id>
<article-id pub-id-type="doi">10.13053/cys-21-2-2742</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Filtrado de ruido Gaussiano mediante redes neuronales pulso-acopladas]]></article-title>
<article-title xml:lang="en"><![CDATA[Using Pulse Coupled Neural Networks to Improve Image Filtering Contaminated with Gaussian Noise]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ortiz Rangel]]></surname>
<given-names><![CDATA[Estela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mejía-Lavalle]]></surname>
<given-names><![CDATA[Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sossa]]></surname>
<given-names><![CDATA[Humberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Centro Nacional de Investigación y Desarrollo Tecnológico Departamento de Ciencias Computacionales ]]></institution>
<addr-line><![CDATA[Cuernavaca Morelos]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Politécnico Nacional Centro de investigación en Computación ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<volume>21</volume>
<numero>2</numero>
<fpage>381</fpage>
<lpage>395</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462017000200381&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462017000200381&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462017000200381&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Se describe un algoritmo llamado ICM-TM para reducir el efecto del ruido Gaussiano en imágenes monocromáticas. La operación del algoritmo se basa en el Modelo de Intersección Cortical (ICM) que es un tipo de Red Neuronal Artificial tipo Pulso-Acoplado. Una matriz de tiempos (TM) proporciona la información correspondiente a la iteración cuando la neurona correspondiente se activa por primera vez. Se establece un criterio de filtrado selectivo que combina el operador de mediana y promedio tomando como base el tiempo de activación de las neuronas. El desempeño del algoritmo propuesto se evaluó experimentalmente con ruido Gaussiano a varios niveles. Los resultados muestran la efectividad de la propuesta con respecto a los filtros mediana, Gaussiano, Sigma, Wiener y las Redes Neuronales Pulso-Acopladas tipo PCNNNI. Los resultados son representados principalmente a través del Cociente Pico Señal a Ruido (CPSR).]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: An algorithm called ICM-TM to reduce the effect of Gaussian noise in grayscale images is proposed. It is based on the operation of the well- known Intersection Cortical Model (ICM), a kind of Pulse-Coupled Artificial Neural Network. A Time Matrix (TM) provides information about the iteration when the neuron fires for first time. Each neuron corresponds to a pixel. A selective filtering criteria that combines the median and average operators using the neuron´s activation time is established. The performance of the proposed algorithm is evaluated experimentally with varying degrees of Gaussian noise. Simulation results show that the effectiveness of the method is superior to the median filter, Gaussian filter, Sigma filter, Wiener filter and to the Pulse-Coupled Neural Networks with the Null Interconnections (PCNNNI). Results are mainly provided by the parameter Peak Signal to Noise Ratio (PSNR).]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Modelo de Intersección Cortical (ICM)]]></kwd>
<kwd lng="es"><![CDATA[ruido Gaussiano]]></kwd>
<kwd lng="es"><![CDATA[filtro Wiener]]></kwd>
<kwd lng="es"><![CDATA[relación pico señal a ruido (PSNR)]]></kwd>
<kwd lng="en"><![CDATA[Intersection Cortical Model (ICM)]]></kwd>
<kwd lng="en"><![CDATA[Gaussian noise]]></kwd>
<kwd lng="en"><![CDATA[Wiener filter]]></kwd>
<kwd lng="en"><![CDATA[Peak Signal to Noise Ratio (PSNR)]]></kwd>
</kwd-group>
</article-meta>
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