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	<title>image restoration Archives &#8212; MATLAB Number ONE</title>
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	<title>image restoration Archives &#8212; MATLAB Number ONE</title>
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		<title>Multiscale KSVD Denoising</title>
		<link>https://matlab1.com/shop/cpp-code/multiscale-ksvd-denoising/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Wed, 16 May 2018 04:22:56 +0000</pubDate>
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					<description><![CDATA[<p>Multiscale KSVD for : &#8211; Denoising &#8211; InPainting &#8211; Demosaicing &#8211; Video Denoising &#8211; Video InPainting &#8211; Video Demosaicing Introduction &#8211; This software is parallel implementation of the three following papers, -# M. Elad and M. Aharon, &#8220;Image Denoising Via Sparse and Redundant representations over Learned Dictionaries&#8221;, the IEEE Trans. on Image Processing, Vol. 15, [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/cpp-code/multiscale-ksvd-denoising/">Multiscale KSVD Denoising</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Expected Patch Log Likelihood (EPLL) for image denoising</title>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Fri, 11 May 2018 05:25:09 +0000</pubDate>
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					<description><![CDATA[<p>Image priors have become a popular tool for image restoration tasks. Good priors have been applied to different tasks such as image denoising , image inpainting and more, yielding excellent results. However, learning good priors from natural images is a daunting task &#8211; the high dimensionality of images makes learning, inference and optimization with such priors prohibitively hard. As a result, in [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/expected-patch-log-likelihood-epll-for-image-denoising/">Expected Patch Log Likelihood (EPLL) for image denoising</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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