Blur Removal Via Blurred-Noisy Image Pair
Blur Removal Via Blurred-Noisy Image Pair
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DOI：
10.1109/tip.2020.3036745
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发表时间：
2021-01-01
影响因子：
10.6
通讯作者：
Zhang, Chao
中科院分区：
文献类型：
--
作者：
Gu, Chunzhi;Lu, Xuequan;Zhang, Chao
真实的图像中普遍存在着空变模糊和空不变模糊混合的复杂模糊，难以用数学方法对其进行建模。在这篇文章中，我们提出了一种新的图像去模糊方法，不需要估计模糊核。我们利用了一对可以在低光照条件下轻松获取的图像：（1）以低快门速度和低ISO噪声拍摄的模糊图像;以及（2）以高快门速度和高ISO噪声拍摄的嘈杂图像。将模糊图像分割成小块，利用噪声图像中对应的小块，扩展高斯混合模型（GMM）来模拟每个小块的强度分布。我们通过分析两幅图像之间的光流计算补丁对应。期望最大化（EM）算法被用来估计GMM的参数。为了保持清晰的特征，我们在M步的目标函数中添加了一个额外的双边项。最后，我们在去模糊图像中添加一个细节层进行细化。对合成数据和真实数据的广泛实验表明，我们的方法在鲁棒性、视觉质量和定量指标方面优于最先进的技术。
Complex blur such as the mixup of space-variant and space-invariant blur, which is hard to model mathematically, widely exists in real images. In this article, we propose a novel image deblurring method that does not need to estimate blur kernels. We utilize a pair of images that can be easily acquired in low-light situations: (1) a blurred image taken with low shutter speed and low ISO noise; and (2) a noisy image captured with high shutter speed and high ISO noise. Slicing the blurred image into patches, we extend the Gaussian mixture model (GMM) to model the underlying intensity distribution of each patch using the corresponding patches in the noisy image. We compute patch correspondences by analyzing the optical flow between the two images. The Expectation Maximization (EM) algorithm is utilized to estimate the parameters of GMM. To preserve sharp features, we add an additional bilateral term to the objective function in the M-step. We eventually add a detail layer to the deblurred image for refinement. Extensive experiments on both synthetic and real-world data demonstrate that our method outperforms state-of-the-art techniques, in terms of robustness, visual quality, and quantitative metrics.