| 引用本文: | 张杰,史小平,张焕龙,耿盛涛.高噪声遥感图像稀疏去噪重建[J].哈尔滨工业大学学报,2019,51(10):47.DOI:10.11918/j.issn.0367-6234.201806161 |
| ZHANG Jie,SHI Xiaoping,ZHANG Huanlong,GENG Shengtao.High noise remote sensing image sparse denoising reconstruction[J].Journal of Harbin Institute of Technology,2019,51(10):47.DOI:10.11918/j.issn.0367-6234.201806161 |
|
| 摘要: |
| 高噪声遥感图像去噪一直是遥感领域研究的一个重要难题, 为进一步提高高噪声遥感图像的重建质量,在经典的压缩感知迭代小波阈值算法的基础上,提出了一种改进迭代小波阈值算法. 首先,提出一种自适应小波滤波算子在图像稀疏变换过程中对获取的遥感图像小波系数进行筛选,去除图像中的部分噪声信息；其次,使用提出的下降BayesShrink阈值在每次迭代过程中对获取的小波系数进行二次筛选过程；最后,使用改进的块稀疏全变差方法对获得的重建图像进行调整以进一步提高重建遥感图像的质量. 试验结果表明,该算法的去噪重建性能优于经典的压缩感知迭代小波阈值算法,可以从高噪声图像中重建一幅高质量的遥感图像,验证了该算法的有效性. 此外,该算法能够有效地保护遥感图像的边缘和纹理等重要特征信息. 在低压缩采样比情况下,该算法也能够获得相对较高的峰值信噪比和视觉质量. 在卫星地面接收站,该算法可直接使用获取的少量含噪遥感图像数据重建一幅清晰的遥感图像. |
| 关键词: 高噪声遥感图像 去噪 压缩感知 小波阈值 改进的块稀疏全变差 |
| DOI：10.11918/j.issn.0367-6234.201806161 |
| 分类号:TN911.73 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(9,7,61873246) |
|
| High noise remote sensing image sparse denoising reconstruction |
|
ZHANG Jie1,SHI Xiaoping2,ZHANG Huanlong1,GENG Shengtao1
|
|
(1.School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China; 2.School of Astronautics, Harbin Institute of Technology, Harbin 150080, China)
|
| Abstract: |
| High noise remote sensing image denoising was a difficult problem in the field of remote sensing research. In this paper, to improve the reconstruction quality of remote sensing images, an improved iterative wavelet thresholding (IWT) algorithm is proposed on the foundation of the classical compressed sensing iterative wavelet thresholding (IWT-CS) algorithm. First, an adaptive wavelet filtering operator was proposed to remove parts of image noise, which selects wavelet coefficients of the remote sensing image in the process of image sparsity transform. Second, a descending BayesShrink threshold was put forward to select the wavelet coefficients obtained again in each iteration. Finally, an improved group sparse total variation (IGSTV) method was utilized to adjust the obtained reconstructed image to further improve the reconstruction quality of the remote sensing image. The experimental result demonstrates that the proposed algorithm was superior to the IWT-CS algorithm in terms of denoising reconstruction, which could recover a high quality remote sensing image from high noise image, and the effectiveness of the proposed algorithm was validated. In addition, the proposed algorithm could effectively protect the edges, textures, and other important feature information in remote sensing image. Under low compression sampling ratio, the proposed algorithm could still obtain relatively high peak signal to noise ratio (PSNR) and visual quality. In the satellite receive station, the proposed algorithm can be directly used to reconstruct a clear remote sensing image using a small amount of received-noise remote sensing image data. |
| Key words: high noise remote sensing image denoising compressed sensing wavelet threshold IGSTV |
