| 引用本文: | 张杰,朱奕,史小平.压缩感知的天文图像去噪算法[J].哈尔滨工业大学学报,2017,49(10):78.DOI:10.11918/j.issn.0367-6234.201609002 |
| ZHANG Jie,ZHU Yi,SHI Xiaoping.Compressedsensing denoising algorithm for astronomical image[J].Journal of Harbin Institute of Technology,2017,49(10):78.DOI:10.11918/j.issn.0367-6234.201609002 |
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| 摘要: |
| 针对压缩感知迭代收缩阈值算法在图像处理中存在收敛速度慢和去噪性能差的缺陷, 提出了一种改进的高性能迭代收缩阈值天文图像去噪重建算法.首先，使用经典最速下降法中的BB线性搜索步长算子加快迭代收缩阈值算法的收敛速度；其次，为了进一步提高重构天文图像的质量，在传统VisuShrink收缩阈值的基础上，提出一种下降VisuShrink收缩阈值对图像信息进行筛选；由于阈值去噪方法在迭代重建的过程中会导致重建的图像中出现伪吉布斯效应，最后采用循环平移的方法在每次迭代过程中对获取的重建图像进行调整.多次的试验结果表明，与传统的压缩感知迭代收缩阈值算法相比, 所提出的算法不仅能够获得较优的去噪性能和较快的收敛速度, 同时可以有效地保护天文图像的特征和纹理等细节信息.此外，当选取的压缩采样比较低时, 本算法也可以获得相对较高的峰值信噪比和视觉质量，进一步验证了本算法在天文图像去噪中的有效性.
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| 关键词: 收缩阈值 天文图像 去噪 压缩感知 循环平移 |
| DOI：10.11918/j.issn.0367-6234.201609002 |
| 分类号:TN911.73 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61074127) |
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| Compressedsensing denoising algorithm for astronomical image |
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ZHANG Jie,ZHU Yi,SHI Xiaoping
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(Control and Simulation Center, Harbin Institute of Technology, Harbin 150080, China)
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| Abstract: |
| In the deep space exploration, astronomical image acquisition, transmission and processing have always been the focus of research. To solve problems of slow convergence speed, poor denoising performance in compressed sensing iterative shrinkage-thresholding algorithm for image processing, an improved iterative shrinkage-thresholding astronomical image denoising and reconstruction algorithm with high performance is proposed. Firstly, the BB linear search stepsize of the classical steepest descent algorithm is used to accelerate the convergence speed of iterative shrinkage-thresholding algorithm; secondly, to further improve the reconstructed astronomical image quality, based on the classical VisuShrink shrinkage-threshold, a decreasing VisuShrink shrinkage-threshold is proposed to select the image information; since the pseudo-gibbs effect caused by threshold denoising method will appear in the process of image reconstruction, the cycle spinning method is finally employed to adjust the reconstructed image in each iteration. Multiple experimental results show that, compared with the traditional compressed sensing iterative shrinkage-thresholding algorithm, the algorithm proposed can not only obtain better denoising performance and faster convergence speed, but also effectively protect the astronomical image detail information, such as feature and texture. In addition, when compression sampling ratio is lower, the algorithm proposed also can obtain relatively higher peak signal to noise ratio and visual quality, proving the effectiveness of the algorithm proposed for astronomical image denoising.
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| Key words: shrinkage-threshold astronomical image denoising compressed sensing cycle spinning |
