SURE-Type Functionals as Criteria for Parametric PSF Estimation
SURE-Type Functionals as Criteria for Parametric PSF Estimation
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DOI：
10.1007/s10851-015-0590-z
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发表时间：
2015
影响因子：
2
通讯作者：
Feng Xue;Jiaqi Liu;Shenghai Jiao;Shengdong Liu;Min Zhao;Zhenhong Niu
中科院分区：
文献类型：
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作者：
Feng Xue;Jiaqi Liu;Shenghai Jiao;Shengdong Liu;Min Zhao;Zhenhong Niu
点扩展函数估计在盲图像反卷积中起着重要的作用。本文提出了基于Stein 's无偏风险估计（SURE）的参数化PSF估计的两个新准则，即预测-SURE及其变体。我们从理论上证明了包含精确（互补）平滑滤波的sure型函数作为PSF估计的有效准则。我们还提供了正则化器近似的理论误差分析，通过分析我们表明，所提出的频率自适应正则化项比其他正则化项产生更准确的PSF估计。特别是，所提出的sure变体使我们能够避免噪声方差的估计，这是传统的sure类函数的关键优势。最后，我们提出了一种有效的准则最小化算法。不限于我们在本文中展示的例子，如果参数化PSF形式可用，所提出的基于sure的框架在其他成像应用中具有很大的潜力。
Point spread function (PSF) estimation plays an important role in blind image deconvolution. This paper proposes two novel criteria for parametric PSF estimation, based on Stein’s unbiased risk estimate (SURE), namely, prediction-SURE and its variant. We theoretically prove the SURE-type functionals incorporating exact (complementary) smoother filtering as the valid criteria for PSF estimation. We also provide the theoretical error analysis for the regularizer approximations, by which we show that the proposed frequency-adaptive regularization term yields more accurate PSF estimate than others. In particular, the proposed SURE-variant enables us to avoid estimation of noise variance, which is a key advantage over the traditional SURE-like functional. Finally, we propose an efficient algorithm for the minimizations of the criteria. Not limited to the examples we show in this paper, the proposed SURE-based framework has a great potential for other imaging applications, provided the parametric PSF form is available.