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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Lu Yuan</author_name><author_url>https://www.microsoft.com/en-us/research/people/luyuan/</author_url><title>Robust Dual Motion Deblurring - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="XPCvBtfDru"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/robust-dual-motion-deblurring/"&gt;Robust Dual Motion Deblurring&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/robust-dual-motion-deblurring/embed/#?secret=XPCvBtfDru" width="600" height="338" title="&#x201C;Robust Dual Motion Deblurring&#x201D; &#x2014; Microsoft Research" data-secret="XPCvBtfDru" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>This paper presents a robust algorithm to deblur two consecutively captured blurred photos from camera shaking. Previous dual motion deblurring algorithms succeeded in small and simple motion blur and are very sensitive to noise. We develop a robust feedback algorithm to perform iteratively kernel estimation and image deblurring. In kernel estimation, the stability and capability [&hellip;]</description></oembed>
