Removing Camera Shake via Weighted Fourier Burst Accumulation
Removing Camera Shake via Weighted Fourier Burst Accumulation
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DOI：
10.1109/tip.2015.2442914
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发表时间：
2015-11-01
影响因子：
10.6
通讯作者：
Sapiro, Guillermo
中科院分区：
文献类型：
--
作者：
Delbracio, Mauricio;Sapiro, Guillermo
最近的许多方法尝试通过显式地解决逆的和固有不适定的反卷积问题来消除由于相机抖动而导致的图像模糊，无论是使用一幅或多幅输入图像。如果摄影师拍摄一系列图像（几乎所有现代数码相机都采用这种模式），我们将证明可以将它们组合起来以获得干净清晰的版本。这是在没有明确解决任何模糊估计和随后的逆问题的情况下完成的。所提出的算法非常简单：它在傅里叶域中执行加权平均，权重取决于傅里叶谱幅度。该方法可以被视为对齐和平均过程的概括，具有加权平均，由握手生理学驱动并得到理论支持，发生在傅立叶域中。该方法的基本原理是相机抖动具有随机性，因此连拍中的每个图像通常会产生不同的模糊效果。对真实相机数据的实验以及广泛的比较表明，所提出的傅里叶突发累积算法以更快的数量级实现了最先进的结果，并且易于在拍照手机上实现。最后，我们还展示了真实高动态范围 (HDR) 场景中的实验，展示了如何将该方法直接扩展到 HDR 摄影。
Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem. If the photographer takes a burst of images, a modality available in virtually all modern digital cameras, we show that it is possible to combine them to get a clean sharp version. This is done without explicitly solving any blur estimation and subsequent inverse problem. The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method can be seen as a generalization of the align and average procedure, with a weighted average, motivated by hand-shake physiology and theoretically supported, taking place in the Fourier domain. The method's rationale is that camera shake has a random nature, and therefore, each image in the burst is generally blurred differently. Experiments with real camera data, and extensive comparisons, show that the proposed Fourier burst accumulation algorithm achieves state-of-the-art results an order of magnitude faster, with simplicity for on-board implementation on camera phones. Finally, we also present experiments in real high dynamic range (HDR) scenes, showing how the method can be straightforwardly extended to HDR photography.