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Graph-Based Blind Image Deblurring From a Single Photograph

机译:单张照片中基于图的盲图像去模糊

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Blind image deblurring, i.e., deblurring without knowledge of the blur kernel, is a highly ill-posed problem. The problem can be solved in two parts: 1) estimate a blur kernel from the blurry image, and 2) given an estimated blur kernel, de-convolve the blurry input to restore the target image. In this paper, we propose a graph-based blind image deblurring algorithm by interpreting an image patch as a signal on a weighted graph. Specifically, we first argue that a skeleton image - a proxy that retains the strong gradients of the target but smooths out the details - can be used to accurately estimate the blur kernel and has a unique bi-modal edge weight distribution. Then, we design a reweighted graph total variation (RGTV) prior that can efficiently promote a bimodal edge weight distribution given a blurry patch. Further, to analyze RGTV in the graph frequency domain, we introduce anew weight function to represent RGTV as a graph l1-Laplacian regularizer. This leads to a graph spectral filtering interpretation of the prior with desirable properties, including robustness to noise and blur, strong piecewise smooth filtering, and sharpness promotion. Minimizing a blind image deblurring objective with RGTV results in a non-convex non-differentiable optimization problem. Leveraging the new graph spectral interpretation for RGTV, we design an efficient algorithm that solves for the skeleton image and the blur kernel alternately. Specifically for Gaussian blur, we propose a further speedup strategy for blind Gaussian deblurring using accelerated graph spectral filtering. Finally, with the computed blur kernel, recent non-blind image deblurring algorithms can be applied to restore the target image. Experimental results demonstrate that our algorithm successfully restores latent sharp images and outperforms the state-of-the-art methods quantitatively and qualitatively.
机译:盲图像去模糊,即在不了解模糊内核的情况下去模糊是一个非常不适的问题。该问题可以分为两个部分解决:1)从模糊图像估计模糊核,以及2)给定估计的模糊核,对卷积模糊的输入进行反卷积以恢复目标图像。在本文中,我们通过将图像块解释为加权图上的信号,提出了一种基于图的盲图像去模糊算法。具体来说,我们首先认为,骨架图像(保留目标的强梯度但平滑细节的代理)可用于准确估计模糊核并具有独特的双峰边缘权重分布。然后,我们设计一个重加权图总变化量(RGTV),在给定模糊补丁的情况下可以有效地促进双峰边缘权重分布。此外,为了在图频域中分析RGTV,我们引入了一个新的权重函数,将RGTV表示为图l n 1 n-Laplacian正则化程序。这导致对现有技术的图形频谱滤波解释具有理想的特性,包括对噪声和模糊的鲁棒性,强大的分段平滑滤波以及增强清晰度。用RGTV最小化模糊图像去模糊物镜会导致非凸不可微优化问题。利用RGTV的新图形频谱解释,我们设计了一种有效的算法,该算法可以交替求解骨架图像和模糊内核。专门针对高斯模糊,我们提出了使用加速图谱滤波对盲高斯去模糊的进一步加速策略。最后,借助计算出的模糊核,可以应用最新的非盲图像去模糊算法来恢复目标图像。实验结果表明,我们的算法成功地恢复了潜在的清晰图像,并且在数量和质量上均优于最新方法。

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