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<records>

  <record>
    <language>eng</language>
          <publisher>Oriental Scientific Publishing Company</publisher>
        <journalTitle>Biomedical and Pharmacology Journal</journalTitle>
          <issn>0974-6242</issn>
            <publicationDate>2015-05-03</publicationDate>
    
        <volume>7</volume>
        <issue>2</issue>

 
    <startPage>707</startPage>
    <endPage>713</endPage>

	 
      <doi>10.13005/bpj/545</doi>
        <publisherRecordId>3274</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">Image Sharpening By Gaussian And Butterworth High Pass Filter</title>

    <authors>
	 


      <author>
       <name>Ayush Dogra </name>

 
		
	<affiliationId>1</affiliationId>
      </author>
    

	 


      <author>
       <name>Parvinder Bhalla</name>


		
	<affiliationId>1</affiliationId>

      </author>
    

	

	


	


	
    </authors>
    
	    <affiliationsList>
	    
		
		<affiliationName affiliationId="1">Department of ECE, Maharishi Markandeshwar University, Mullana, Ambala, India.</affiliationName>
    

		
		
		
		
		
	  </affiliationsList>






    <abstract language="eng">Many significant images contains some extent of noise, that is unexplained variation in data disturbances in image intensity which are either uninterpretable or out of interest. Image analysis is often simplified if this unwanted noise is filtered. For this image simplification, filtering in frequency domain is done. In this paper we will demonstrate the image sharpening by Gaussian &amp; Butterworth high pass filter and jot some points revealing their differences.</abstract>

    <fullTextUrl format="html">https://biomedpharmajournal.org/vol7no2/image-sharpening-by-gaussian-and-butterworth-high-pass-filter/</fullTextUrl>

<keywords language="eng">

      
        <keyword>Gaussian</keyword>
      

      
        <keyword> Butterworth</keyword>
      

      
        <keyword> image analysis</keyword>
      

      
        <keyword> frequency domain</keyword>
      
</keywords>
  </record>
</records>