Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi, China.
University of Chinese Academy of Sciences, Beijing, China.
PLoS One. 2020 Mar 27;15(3):e0230619. doi: 10.1371/journal.pone.0230619. eCollection 2020.
In imaging systems, image blurs are a major source of degradation. This paper proposes a parameter estimation technique for linear motion blur, defocus blur, and atmospheric turbulence blur, and a nonlinear deconvolution algorithm based on sparse representation. Most blur removal techniques use image priors to estimate the point spread function (PSF); however, many common forms of image priors are unable to exploit local image information fully. In this paper, the proposed method does not require models of image priors. Further, it is capable of estimating the PSF accurately from a single input image. First, a blur feature in the image gradient domain is introduced, which has a positive correlation with the degree of blur. Next, the parameters for each blur type are estimated by a learning-based method using a general regression neural network. Finally, image restoration is performed using a half-quadratic optimization algorithm. Evaluation tests confirmed that the proposed method outperforms other similar methods and is suitable for dealing with motion blur in real-life applications.
在成像系统中，图像模糊是主要的退化源。本文提出了一种用于线性运动模糊、散焦模糊和大气湍流模糊的参数估计技术，以及一种基于稀疏表示的非线性反卷积算法。大多数去模糊技术都使用图像先验来估计点扩散函数 (PSF)；然而，许多常见的图像先验形式都无法充分利用局部图像信息。在本文中，所提出的方法不需要图像先验模型。此外，它能够从单个输入图像准确地估计 PSF。首先，在图像梯度域中引入了一个模糊特征，该特征与模糊程度呈正相关。接下来，使用基于学习的方法（使用广义回归神经网络）来估计每个模糊类型的参数。最后，使用半二次优化算法进行图像恢复。评估测试证实，所提出的方法优于其他类似方法，适用于处理现实生活中的运动模糊。
