压缩感知的高分辨率天文图像去噪

发布时间：2018-05-26 22:32

本文选题：高分辨率 + 天文图像　；参考：《哈尔滨工业大学学报》2017年04期

【摘要】：为提高高分辨率天文图像的重构质量,在传统压缩感知(compressed sensing,CS)迭代小波阈值算法的基础上,提出了一种基于小波维纳滤波的压缩感知去噪重构算法.该算法的设计方法为:在每次迭代过程中,使用设计的小波维纳滤波算子替代传统的小波阈值算子对获得的天文图像小波系数进行筛选,从而对小波阈值去噪方法重建图像过程中出现的伪吉布斯现象进行有效地抑制;然后使用全变差方法对去噪重建后的天文图像进行调整,以进一步提高重构图像的质量.仿真实验结果表明,与传统的迭代小波阈值算法相比,本算法可以获得较优的去噪重建性能,并且能有效地保护高分辨率天文图像的细节特征信息.此外,在压缩比较高的情况下,该算法仍然可以获得相对较高的视觉质量和峰值信噪比.
[Abstract]:In order to improve the reconstruction quality of high-resolution astronomical images, based on the traditional compressed sensing CS-based iterative wavelet threshold algorithm, a new algorithm based on wavelet Wiener filter is proposed. The design method of the algorithm is as follows: in each iteration, the wavelet coefficients of astronomical images are filtered by using the designed wavelet Wiener filter operator instead of the traditional wavelet threshold operator. The pseudo-Gibbs phenomenon in the reconstruction process of wavelet threshold denoising method is effectively suppressed, and then the total variation method is used to adjust the reconstructed astronomical image in order to further improve the quality of reconstructed image. The simulation results show that compared with the traditional iterative wavelet threshold algorithm, this algorithm can obtain better denoising and reconstruction performance, and can effectively protect the detailed feature information of high-resolution astronomical images. In addition, in the case of high compression, the algorithm can still achieve relatively high visual quality and peak signal-to-noise ratio (PSNR).
【作者单位】：哈尔滨工业大学控制与仿真中心;上海航天技术研究院;
【基金】：国家自然科学基金(61427809)
【分类号】：TP391.41

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