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Multi-frame blind deconvolution of solar images via second-order total generalized variation
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摘要:
盲解卷积是常用的自适应光学图像事后重建方法之一。为提高盲解卷积对太阳(自适应光学)图像的重建效果，本文提出了基于二阶广义总变分的空变多帧盲解卷积算法。该算法首先利用交替最小化和半二次分裂方法求解本文提出的二阶广义总变分约束的空不变多帧盲解卷积模型；然后针对非等晕大视场太阳图像特性，利用重叠分块与加权拼接实现空变盲解卷积扩展。在一米新真空太阳望远镜(NVST)观测的真实太阳图像上进行的重建实验与分析表明，本文算法在主观视觉效果和客观指标上均具有较好的图像重建效果。
Abstract:Blind deconvolution is one of the commonly used post-reconstruction methods for adaptive optics images. In order to improve the reconstruction performance of blind deconvolution on solar (adaptive optics) images, a space-variant multi-frame blind deconvolution model based on second-order total generalized variation is proposed. It first solves the proposed space-invariant blind deconvolution model via second-order total generalized variation by the alternating minimization and half-quadratic splitting method. Then, according to the characteristics of wide field-of-view solar images which are anisoplanatic, the space-variant in the proposed algorithm is implemented by overlapping image segmentation and weighted stitching. Finally, the reconstruction experiment and analysis are carried out on the real solar images observed by the one-meter New Vacuum Solar Telescope (NVST). The results show that the proposed algorithm has good image reconstruction performance in both subjective visual effects and objective indexes.
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图 2 大视场图像重建结果。(a) 输入序列中总体质量最好帧；(b) TVBD；(c) S-TGV；(d) OBD；(e) M-TGV；(f) 斑点重建
Figure 2. Reconstruction results of the wide field-of-view image. (a) The image with the best overall quality in the input sequence; (b) TVBD; (c) S-TGV; (d) OBD; (e) M-TGV; (f) Speckle reconstruction
图 3 大视场图像子区域重建结果。(a) 输入序列中总体质量最好帧；(b) TVBD；(c) S-TGV；(d) OBD；(e) M-TGV；(f) 斑点重建
Figure 3. Subregion reconstruction results of the wide field-of-view image. (a) The image with the best overall quality in the input sequence; (b) TVBD; (c) S-TGV; (d) OBD; (e) M-TGV; (f) Speckle reconstruction
图 4 不同输入帧数的重建结果。(a) 输入序列的其中1帧；(b) ~ (g) 分别对应输入1、2、3、5、10、20帧的重建结果；(h) 斑点重建
Figure 4. Reconstruction results of different input frames. (a) One frame in the input sequence; (b) ~ (g) The reconstruction results corresponding to input 1, 2, 3, 5, 10 and 20 frames respectively (h) Speckle reconstruction
图 5 图像正则项的有效性。(a) 输入序列的其中一帧；(b) 无正则项；(c) M-TGV；(d) 斑点重建
Figure 5. The effectiveness of image regularization. (a) One frame in the input sequence; (b) Without regularization; (c) M-TGV; (d) Speckle reconstruction
图 6 不同K和β1的重建图像和估计的PSF。(a) K=1, β1=0；(b) K=1, β1=10；(c) K=5, β1=0；(d) K=5, β1=10
Figure 6. Reconstructed images and estimated PSF with different K and β1 values. (a) K=1, β1=0; (b) K=1, β1=10; (c) K=5, β1=0; (d) K=5, β1=10
图 7 目标函数迭代曲线。(a) 尺度层4；(b) 尺度层3；(c) 尺度层2；(d) 尺度层1
Figure 7. Iteration curves of the objective function. (a) Fourth scale; (b) Third scale; (c) Second scale; (d) First scale
表 1 重建结果定量评价(PSNR(dB)/SSIM)
Table 1. Quantitative evaluation of reconstruction results (PSNR(dB)/SSIM)
输入序列中总
体质量最好帧TVBD S-TGV OBD M-TGV 米粒1 32.68/0.9060 32.05/0.9058 32.27/0.9127 32.70/0.9082 32.79/0.9173 米粒2 33.63/0.9198 33.60/0.9269 33.45/0.9302 34.00/0.9219 34.48/0.9396 半影 29.41/0.8434 29.00/0.8424 29.57/0.8576 29.70/0.8542 30.38/0.8785 本影 29.65/0.8771 29.68/0.8721 30.08/0.8927 30.09/0.8843 30.30/0.8975 整帧图像 31.06/0.8806 30.77/0.8793 30.96/0.8892 31.25/0.8855 31.31/0.8969 
下载: 导出CSV
表 2 算法运行时间
Table 2. Running time of different algorithms
算法 TVBD S-TGV OBD M-TGV 时间/s 715.37 12.35 156.61 27.68
下载: 导出CSV
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