基于非参数贝叶斯遥感影像超分辨率的改进算法

Improved Algorithm of Remote Sensing Images Super-Resolution Based on Nonparametric Bayesian

为了提高遥感影像的空间分辨率,将用于自然影像超分辨率重建的非参数贝叶斯字典学习模型引入到遥感影像处理领域,提出了一种基于非参数贝叶斯和纹理分块的单幅遥感影像超分辨率重建的改进方法。该方法利用BetaBernoulli process进行字典学习,建立字典元素和各参数的概率分布模型,并使用Gibbs抽样计算其后验分布。最后,在重构时先将影像块分为平滑块和非平滑块两种类型,对非平滑块利用高分辨率字典的后验分布及低分辨率影像块的稀疏系数重建出高分辨率遥感影像,而对平滑块仅采用双三次卷积方法进行重构。此外,区别于传统算法需事先设置较大维数字典以保证较高重建精度的不足,对字典维数进行非参数推导,获得较小维数字典,减少了运算量。实验表明,不论测试影像有无噪声,所提算法在视觉及定量评价指标上较传统方法均有改善,且重构速度较快。

In order to improve the spatial resolution of remote sensing images, the nonparametric Bayesian dictionary learning model for natural images super-resolution reconstruction is introduced into the field of remote sensing image processing. Based on nonparametric Bayesian and classified texture patches, an improved method of the single remote sensing image super-resolution reconstruction is proposed. The method uses the Beta-Bernoulli process for dictionary learning, and establishes the probability distribution models of dictionary elements and parameters. The Gibbs sampling is used to calculate the posterior distribution. Finally, the image block is divided into two types： smooth block and non-smooth block during reconstruction. The non-smooth block reconstructs the high resolution remote sensing image by using the posterior distribution of the high-resolution dictionary and the sparse coefficients of the low-resolution image blocks. While the smooth block only uses the bieubic convolution method to reconstruct. Furthermore, different from the shortage of traditional algorithm that needs to set a large dimension dictionary in advance to ensure a higher reconstruction precision, a smaller dimension dictionary is obtained by non-parametrical deviation of dictionary dimension in this paper, which reduces the calculation. The results show that the proposed algorithm outperforms traditional approaches both in visual and quantitative evaluation indexes whether the test image is noisy, and the reconstruction speed is faster.