Enhancing Bayesian Estimators for Removing Camera Shake
Enhancing Bayesian Estimators for Removing Camera Shake
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DOI：
10.1111/cgf.12074
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发表时间：
2013-09
影响因子：
2.5
通讯作者：
Chao Wang-;Yong Yue;F. Dong;Y. Tao;Xiaojun Ma;G. Clapworthy;Xujiong Ye
中科院分区：
文献类型：
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作者：
Chao Wang-;Yong Yue;F. Dong;Y. Tao;Xiaojun Ma;G. Clapworthy;Xujiong Ye
消除相机抖动的目的是当模糊核 k 未知时从抖动图像 y 估计清晰版本 x。最近关于这个主题的研究通过称为 MAP(k) 和 MAP(x,k) 的两种范式演变而来。 MAP(k) 仅通过边缘化图像先验来求解 k，而 MAP(x,k) 通过选择后验分布的模式来恢复 x 和 k。本文首先通过贝叶斯分析系统地分析了这两种估计量的潜在局限性。我们解释了为什么图像统计很难解决之前报道的 MAP(x,k) 故障。然后我们表明，领先的 MAP(x,k) 方法依赖于大步长边缘的有效预测，由于边缘的多样性，对自然图像不具有鲁棒性。 MAP(k) 虽然对不同的边缘更加鲁棒，但受到两个因素的限制：不同图像的先验变化，以及图像大小与内核大小之间的比率。为了克服这些限制，我们引入了一种尺度间先验预测方案和一种将锐化滤波器集成到 MAP(k) 中的原理机制。定性结果和广泛的定量比较都表明我们的算法优于最先进的方法。
The aim of removing camera shake is to estimate a sharp version x from a shaken image y when the blur kernel k is unknown. Recent research on this topic evolved through two paradigms called MAP(k) and MAP(x,k) . MAP(k) only solves for k by marginalizing the image prior, while MAP(x,k) recovers both x and k by selecting the mode of the posterior distribution. This paper first systematically analyses the latent limitations of these two estimators through Bayesian analysis. We explain the reason why it is so difficult for image statistics to solve the previously reported MAP(x,k) failure. Then we show that the leading MAP(x,k) methods, which depend on efficient prediction of large step edges, are not robust to natural images due to the diversity of edges. MAP(k) , although much more robust to diverse edges, is constrained by two factors: the prior variation over different images, and the ratio between image size and kernel size. To overcome these limitations, we introduce an inter‐scale prior prediction scheme and a principled mechanism for integrating the sharpening filter into MAP(k) . Both qualitative results and extensive quantitative comparisons demonstrate that our algorithm outperforms state‐of‐the‐art methods.