Shift-Variant Blind Deconvolution Using a Field of Kernels
Shift-Variant Blind Deconvolution Using a Field of Kernels
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DOI：
10.1587/transinf.2016pcp0013
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发表时间：
2017-09
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通讯作者：
Motoharu Sonogashira;M. Iiyama;M. Minoh
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作者：
Motoharu Sonogashira;M. Iiyama;M. Minoh
盲解卷积（BD）是在卷积核未知的情况下从模糊图像恢复清晰图像的问题。虽然它有广泛的应用范围，并已被广泛研究，传统的移位不变（SI）BD专注于不空间变化的内核所造成的均匀模糊。然而，由诸如运动和散焦等因素引起的真实的模糊通常是不均匀的，因此超出了SI BD的能力。尽管存在用于非均匀模糊的专用方法，但它们只能处理特定的模糊类型。因此，BD对于一般模糊的适用性仍然有限。本文提出了一种移变（SV）BD方法，该方法使用一个内核字段，为每个像素分配一个本地内核，从而允许逐像素变化的非均匀模糊模型。该概念被实现为贝叶斯模型，其涉及与核的场的SV卷积和用于正则化的场的平滑。一个variationalBayesian推理算法推导出联合估计一个清晰的潜像和一个领域的内核从一个模糊的观察图像。由于核场模型的灵活性，该方法可以处理更广泛的模糊比以前的方法。使用图像的非均匀模糊的实验表明，与以前的SI和SV方法相比，所提出的SV BD方法的有效性。关键词：盲反卷积，去模糊，移变，变分贝叶斯
Blind deconvolution (BD) is the problem of restoring sharp images from blurry images when convolution kernels are unknown. While it has a wide range of applications and has been extensively studied, traditional shift-invariant (SI) BD focuses on uniform blur caused by kernels that do not spatially vary. However, real blur caused by factors such as motion and defocus is often nonuniform and thus beyond the ability of SI BD. Although specialized methods exist for nonuniform blur, they can only handle specific blur types. Consequently, the applicability of BD for general blur remains limited. This paper proposes a shift-variant (SV) BD method that models nonuniform blur using a field of kernels that assigns a local kernel to each pixel, thereby allowing pixelwise variation. This concept is realized as a Bayesian model that involves SV convolution with the field of kernels and smoothing of the field for regularization. A variationalBayesian inference algorithm is derived to jointly estimate a sharp latent image and a field of kernels from a blurry observed image. Owing to the flexibility of the field-of-kernels model, the proposed method can deal with a wider range of blur than previous approaches. Experiments using images with nonuniform blur demonstrate the effectiveness of the proposed SV BD method in comparison with previous SI and SV approaches. key words: blind deconvolution, deblurring, shift-variant, variational Bayes