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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1608.03981/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1608.03981/feed.xml" rel="self" type="application/rss+xml" /><item><title>Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising — mentioned by protomok</title><link>https://news.ycombinator.com/item?id=15792161</link><guid isPermaLink="false">urn:hackerpapers:mention:1608.03981:15792161</guid><pubDate>Mon, 27 Nov 2017 22:23:52 GMT</pubDate><description>&lt;p&gt;Great summary of recent Computer Vision. Over 203 references! One exciting topic I did not see mentioned (maybe I missed it) is image denoising. Excellent paper here - https://arxiv.org/abs/1608.03981. The CNN proposed in the paper is really compute intensive but the denoised images are really amazing.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1608.03981/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
