中国激光, 2020, 47 (2): 0207024, 网络出版: 2020-02-21 复制并引用该论文  本文已被引 8 次  被引通知

图 & 表

图 1. NC-PCA算法处理流程图

Fig. 1. Workflow diagram of NC-PCA algorithm

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图 2. 仿真降噪图像结果。(a)原始图像;(b)添加背景噪声和散粒噪声后的图像;(c)选取1~3个主成分时的降噪图像;(d)选取1~6个主成分时的降噪图像;(e)选取1~9个主成分时的降噪图像;(f)图2(a)~(e)中虚线处对应的强度分布曲线;(g)选取不同主成分的降噪图像的PSNR和SSIM曲线;(h)选取不同主成分的降噪图像的CEVR曲线

Fig. 2. Image denoising simulation. (a) Original image; (b) image with background noise and shot noise; (c) denoised image obtained by 1--3 principal components; (d) denoised image obtained by 1--6 principal components; (e) denoised image obtained by 1--9 principal components; (f) normalized intensity curves corresponding to dotted lines in Fig.2(a)--(e); (g) PSNR and SSIM curves for denoised images with different principal component numbers; (h) CEVR for denoised im

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图 3. NC-PCA算法对不同信噪比图像的影响。(a) NC-PCA处理前后,图像的PSNR曲线;(b)图像的CSSTORM定位的E_RMS

Fig. 3. Effect of NC-PCA algorithm on images with different SNRs. (a) PSNR curves before and after NC-PCA algorithm processing; (b) E_RMS of CSSTORM localization of images

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图 4. NC-PCA算法处理前后探针的光致闪烁图像。(a)原始闪烁图像;(b) NC-PCA方法选取1~6个主成分得到的降噪图像;(c) NC-PCA方法选取1~3个主成分得到的降噪图像

Fig. 4. Photo scintillation images before and after NC-PCA algorithm processing. (a) Original flashing image; (b) denoised image obtained by NC-PCA processing with 1--6 principal components; (c) denoised image obtained by NC-PCA processing with 1--3 principal components

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图 5. 不同分析算法对不同帧数数据重构处理的超分辨结果比较。(a) 500 frame数据,比例尺:2 μm;(b)图5(a)方框区域的放大图,比例尺:0.5 μm;(c)数据为200 frame时,图5(a)方块区域的放大图,比例尺:0.5 μm

Fig. 5. Comparison of super-resolution results of different algorithms for data reconstruction with different frames. (a) Reconstructed 500-frame data, scale bar: 2 μm; (b) magnified view of Fig.5(a), scale bar: 0.5 μm; (c) magnified view of Fig.5(a) with the data reconstruction using 200 frame, scale bar: 0.5 μm

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图 6. 图5虚线处的光子数分布图。(a)图5(b)第1列图像;(b)图5(b)第2列图像;(c)图5(b)第3列图像

Fig. 6. Photon number distribution along the dashed lines in Fig.5. (a) First column image in Fig.5(b); (b) second column image in Fig.5(b); (c) third column image in Fig.5(b)

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图 7. 图5(a)各方法的FRC空间分辨率分析曲线。(a)原始图像;(b) K-因子预处理;(c) NC-PCA降噪和K-因子预处理

Fig. 7. FRC spatial resolution analysis curves of methods in Fig.5(a). (a) Raw image; (b) K-factor preprocessing; (c) NC-PCA noise reduction and K-factor pre-processing

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图 8. 线粒体外膜的降噪效果图。(a)线粒体外膜宽场图像;(b) CSSTORM重构获得的图像;(c)经NC-PCA降噪和K-因子预处理后,CSSTORM重构获得的图像;(d)图8(c)方形框区域CSSTORM重构的放大图像;(e)经NC-PCA降噪和K-因子预处理后,图8(c)方形框区域CSSTORM重构的放大图像;(f)沿图8(d)中实线得到的横截面强度归一化后的高斯拟合分布图;(g)沿着图8(e)中实线得到的横截面强度归一化后的高斯拟合分布图。图8(a)~(c)比例尺: 2 μm,图8(d)(e)比例尺:500 nm

Fig. 8. Noise reduction effect of outer mitochondrial membrane. (a) Wide-field image of outer mitochondrial membrane; (b) image obtained by CSSTORM reconstruction; (c) image obtained by CSSTORM reconstruction after NC-PCA noise reduction and K-factor pretreatment; (d) magnified image of the rectangle area in Fig.8(c) obtained by CSSTORM reconstruction; (e) magnified image of the rectangle area in Fig.8(c) obtained by CSSTOR

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潘文慧, 陈秉灵, 张建国, 顾振宇, 熊佳, 张丹, 杨志刚, 屈军乐. 基于噪声校正主成分分析的压缩感知STORM超分辨图像重构[J]. 中国激光, 2020, 47(2): 0207024. Pan Wenhui, Chen Bingling, Zhang Jianguo, Gu Zhenyu, Xiong Jia, Zhang Dan, Yang Zhigang, Qu Junle. Compressed Sensing STORM Super-Resolution Image Reconstruction Based on Noise Correction-Principal Component Analysis Preprocessing Algorithm[J]. Chinese Journal of Lasers, 2020, 47(2): 0207024.