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The spatial correlation problem of noise in imaging deblurring and its solution

机译：图像去模糊中噪声的空间相关性问题及其解决方案

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摘要

We describe the spatial correlation problem of noise in colour digital images and analyse its cause. Pixel correlated image processing procedures, such as CFA colour interpolation and colour space transformation, mainly lead to this problem. Considering this problem, we propose a new noise model based on a joint Gaussian probability distribution. Furthermore, we present an algorithm that makes the revised noise model fit the existing image deconvolution well. The parameters of our algorithm depend only on the image processing procedures of the imaging system. Finally, we apply the proposed algorithm to revise two typical image deconvolution methods and perform simulations and real-world experiments. Both the quantitative indicators and visual performance of the image deblurring results show that the revised deconvolution methods based on our noise model behave better in reducing the noise and ringing artefacts, thus improving the image quality compared with the methods that use the original noise model. (C) 2018 Elsevier Inc. All rights reserved.
机译：我们描述了彩色数字图像中噪声的空间相关性问题，并分析了其原因。像素相关的图像处理过程（例如CFA颜色插值和颜色空间变换）主要导致此问题。考虑到这个问题，我们提出了一种基于联合高斯概率分布的新噪声模型。此外，我们提出了一种算法，可以使修正后的噪声模型很好地适合现有的图像反卷积。我们算法的参数仅取决于成像系统的图像处理程序。最后，我们将提出的算法应用于两种典型的图像反卷积方法，并进行仿真和实际实验。图像去模糊结果的定量指标和视觉性能都表明，基于我们的噪声模型的改进的去卷积方法在减少噪声和振铃伪影方面表现得更好，从而与使用原始噪声模型的方法相比，可以改善图像质量。 （C）2018 Elsevier Inc.保留所有权利。

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