Generalizing the Nonlocal-Means to Super-Resolution Reconstruction
Generalizing the Nonlocal-Means to Super-Resolution Reconstruction
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DOI：
10.1109/tip.2008.2008067
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发表时间：
2009-01-01
影响因子：
10.6
通讯作者：
Milanfar, Peyman
中科院分区：
文献类型：
--
作者：
Protter, Matan;Elad, Michael;Milanfar, Peyman
超分辨率重建提出了一个融合的几个低质量的图像到一个更高的质量与更好的光学分辨率的结果。经典的超分辨率技术强烈依赖于精确的运动估计的可用性，这种融合任务。当运动被不准确地估计时，如经常发生在非线性运动场中，在超分辨结果中出现令人讨厌的伪影。鼓励最近的事态发展的视频去噪问题，其中国家的最先进的算法形成没有明确的运动估计，我们寻求一个超分辨率算法的类似性质，将允许处理序列与一般的运动模式。在本文中，我们基于我们的解决方案的非局部均值（NLM）算法。我们展示了如何将这种去噪方法推广到一个相对简单的超分辨率算法，没有显式的运动估计。实验结果表明，该方法在一般序列图像的超分辨率处理上取得了很好的效果。
Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inaccurately, as often happens for nonglobal motion fields, annoying artifacts appear in the super-resolved outcome. Encouraged by recent developments on the video denoising problem, where state-of-the-art algorithms are formed with no explicit motion estimation, we seek a super-resolution algorithm of similar nature that will allow processing sequences with general motion patterns. In this paper, we base our solution on the Nonlocal-Means (NLM) algorithm. We show how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation. Results on several test movies show that the proposed method is very successful in providing super-resolution on general sequences.