【摘要】：在天文圖像去噪中,為了提高迭代曲波閾值算法的去噪重建性能,提出了基于循環(huán)平移和曲波維納濾波的壓縮感知迭代重構(gòu)算法。首先,使用基于曲波閾值的循環(huán)平移方法對重構(gòu)圖像進(jìn)行調(diào)整以抑制重構(gòu)圖像中的偽吉布斯效應(yīng);接著,用提出的曲波維納濾波算子替代小波閾值在迭代過程中對圖像曲波系數(shù)進(jìn)行篩選以進(jìn)一步提高重構(gòu)圖像的質(zhì)量。通過對添加高斯白噪聲的Lena圖像和月球圖像進(jìn)行重構(gòu)實驗,分析本文算法和當(dāng)前主流算法的性能。實驗結(jié)果表明,與傳統(tǒng)的壓縮感知迭代曲波閾值算法相比,本文算法能夠獲得較優(yōu)的去噪性能,有效地保護(hù)天文圖像的細(xì)節(jié)信息,峰值信噪比大約提高了2.6~3.2dB。
[Abstract]:In order to improve the performance of iterative Qu Bo threshold algorithm in astronomical image denoising, a compression sensing iterative reconstruction algorithm based on cyclic translation and Qu Bo Wiener filter is proposed. Firstly, the cyclic translation method based on Qu Bo threshold is used to adjust the reconstructed image to suppress the pseudo-Gibbs effect in the reconstructed image. Then, the proposed Qu Bo Wiener filter operator is used instead of the wavelet threshold in the iterative process to filter the Qu Bo coefficient of the image to further improve the quality of the reconstructed image. Based on the experiments of Lena and moon images with Gao Si white noise, the performance of this algorithm and the current mainstream algorithms are analyzed. The experimental results show that compared with the traditional compression sensing iterative Qu Bo threshold algorithm, the proposed algorithm can obtain better denoising performance and effectively protect the detailed information of astronomical images.
【作者單位】： 哈爾濱工業(yè)大學(xué)控制與仿真中心;
【基金】：國家自然科學(xué)基金資助項目(No.61074127)
【分類號】：TP391.41

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本文編號：2302817