Universal reconstruction method for radiometric quality improvement of remote sensing images
Universal reconstruction method for radiometric quality improvement of remote sensing images
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DOI：
10.1016/j.jag.2010.04.002
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
Huanfeng Shen;Yaolin Liu;T. Ai;Yi Wang;Bo Wu
中科院分区：
文献类型：
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作者：
Huanfeng Shen;Yaolin Liu;T. Ai;Yi Wang;Bo Wu
在某些应用中，遥感图像的性能往往受到噪声、模糊、条纹和损坏像素的存在以及传感器在空间分辨率方面的硬件限制的影响。本文提出了一种通用的重建方法，可以用来提高图像质量，通过执行图像去噪，反卷积，去噪，修复，插值和超分辨率重建。该方法由两部分组成：一个通用的图像观测模型和一个通用的图像重建模型。在观测模型中，大多数退化过程中的遥感成像被认为是为了相关的期望图像的观测图像。对于重建模型，我们使用最大后验概率（MAP）框架来建立最小化能量方程。基于图像观测模型构造似然概率密度函数，并采用鲁棒的Huber-Markov模型作为先验概率密度函数。实验结果表明了该方法的有效性。
The performance of remote sensing images in some applications is often affected by the existence of noise, blurring, stripes and corrupted pixels, as well as the hardware limits of the sensor with respect to spatial resolution. This paper presents a universal reconstruction method that can be used to improve the image quality by performing image denoising, deconvolution, destriping, inpainting, interpolation and super-resolution reconstruction. The proposed method consists of two parts: a universal image observation model and a universal image reconstruction model. In the observation model, most degradation processes in remote sensing imaging are considered in order to relate the desired image to the observed images. For the reconstruction model, we use the maximum a posteriori (MAP) framework to set up the minimization energy equation. The likelihood probability density function (PDF) is constructed based on the image observation model, and a robust Huber–Markov model is employed as the prior PDF. Experimental results are presented to illustrate the effectiveness of the proposed method.