Computer Science Department, Faculty of Mathematics and Computer, Higher Education Complex of Bam, Bam, Iran.
Image Processing and Data Mining (IPDM) Research Lab, Faculty of Computer Engineering and Information Technology, Shahrood University of Technology, Shahrood, Iran.
Comput Math Methods Med. 2021 Oct 28;2021:6048137. doi: 10.1155/2021/6048137. eCollection 2021.
Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.
运动模糊是图像处理中的一种常见伪影，特别是在电子健康服务中，它是由相机或场景的运动引起的。在线性运动情况下，模糊核，即模拟线性运动模糊过程的函数，取决于模糊的长度和方向，称为线性运动模糊参数。模糊参数的估计是重构运动模糊图像清晰版本的过程中的一个重要和敏感的阶段，即图像去模糊。模糊参数的估计也可以用于电子健康服务。由于医学图像可能模糊，这种方法可以用于估计模糊参数，然后采取行动来增强图像。本文提出了一些基于从给定的单个模糊图像中提取特征来估计线性运动模糊参数的方法。使用模糊图像频谱的 Radon 变换来估计运动模糊方向。为了估计运动模糊长度，应用了一种模糊度量，称为 NIDCT（基于噪声免疫离散余弦变换）与运动模糊长度之间的关系。本研究中的实验表明，NIDCT 模糊度量和模糊长度之间存在单调关系。确实，模糊长度的增加会导致通过 NIDCT 模糊度量估计的模糊值增加。这种关系用于估计运动模糊。通过进行一些定量和定性实验，证明了所提出方法的效率。
