A MAP-Based Algorithm for Destriping and Inpainting of Remotely Sensed Images
A MAP-Based Algorithm for Destriping and Inpainting of Remotely Sensed Images
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一种基于MAP的遥感图像去条纹和修复算法
DOI：
10.1109/tgrs.2008.2005780
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发表时间：
2009-05-01
影响因子：
8.2
通讯作者：
Zhang, Liangpei
中科院分区：
文献类型：
--
作者：
Shen, Huanfeng;Zhang, Liangpei
遥感图像经常会遇到条纹噪声和随机坏点等问题。从受污染的图像中恢复良好图像的技术被称为图像条带化（对于条纹）和图像修复（对于死像素）。本文提出了一种基于最大后验概率（MAP）的去噪和修复算法。该算法的主要优点是在去噪和修复过程中可以根据先验知识对解空间进行约束。在MAP框架中，基于线性图像观测模型构造似然概率密度函数（PDF），并使用鲁棒的Huber-Markov模型作为先验PDF。采用梯度下降优化方法来产生所需的图像。使用中等分辨率成像光谱仪图像进行条纹去除，并使用中巴地球资源卫星和QuickBird图像进行模拟修复，对所提出的算法进行了测试。实验结果和定量分析验证了该算法的有效性。
Remotely sensed images often suffer from the common problems of stripe noise and random dead pixels. The techniques to recover a good image from the contaminated one are called image destriping (for stripes) and image inpainting (for dead pixels). This paper presents a maximum a posteriori (MAP)-based algorithm for both destriping and inpainting problems. The main advantage of this algorithm is that it can constrain the solution space according to a priori knowledge during the destriping and inpainting processes. In the MAP framework, the likelihood probability density function (PDF) is constructed based on a linear image observation model, and a robust Huber-Markov model is used as the prior PDF. The gradient descent optimization method is employed to produce the desired image. The proposed algorithm has been tested using moderate resolution imaging spectrometer images for destriping and China-Brazil Earth Resource Satellite and QuickBird images for simulated inpainting. The experiment results and quantitative analyses verify the efficacy of this algorithm.