Jafaei Mahdi, Monadjemi Amirhassan, Moallem Payman, Ehsani Mohammad Saeed
Department of Artificial Intelligence, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
School of Continuing and Lifelong Education, National University of Singapore, Kent Ridge, Singapore.
PeerJ Comput Sci. 2024 Jan 31;10:e1713. doi: 10.7717/peerj-cs.1713. eCollection 2024.
In this article, a novel method for removing atmospheric turbulence from a sequence of turbulent images and restoring a high-quality image is presented. Turbulence is modeled using two factors: the geometric transformation of pixel locations represents the distortion, and the varying pixel brightness represents spatiotemporal varying blur. The main framework of the proposed method involves the utilization of low-rank matrix factorization, which achieves the modeling of both the geometric transformation of pixels and the spatiotemporal varying blur through an iterative process. In the proposed method, the initial step involves the selection of a subset of images using the random sample consensus method. Subsequently, estimation of the mixture of Gaussian noise parameters takes place. Following this, a window is chosen around each pixel based on the entropy of the surrounding region. Within this window, the transformation matrix is locally estimated. Lastly, by considering both the noise and the estimated geometric transformations of the selected images, an estimation of a low-rank matrix is conducted. This estimation process leads to the production of a turbulence-free image. The experimental results were obtained from both real and simulated datasets. These results demonstrated the efficacy of the proposed method in mitigating substantial geometrical distortions. Furthermore, the method showcased the ability to improve spatiotemporal varying blur and effectively restore the details present in the original image.
本文提出了一种从一系列湍流图像中去除大气湍流并恢复高质量图像的新方法。使用两个因素对湍流进行建模：像素位置的几何变换表示失真，而变化的像素亮度表示时空变化的模糊。所提出方法的主要框架涉及利用低秩矩阵分解，通过迭代过程实现对像素几何变换和时空变化模糊的建模。在所提出的方法中，初始步骤是使用随机抽样一致性方法选择图像子集。随后，估计高斯噪声参数的混合。在此之后，根据周围区域的熵在每个像素周围选择一个窗口。在这个窗口内，局部估计变换矩阵。最后，通过考虑所选图像的噪声和估计的几何变换，进行低秩矩阵的估计。这个估计过程产生了无湍流的图像。实验结果来自真实和模拟数据集。这些结果证明了所提出方法在减轻严重几何失真方面的有效性。此外，该方法还展示了改善时空变化模糊并有效恢复原始图像中细节的能力。
