光学学报, 2018, 38 (10): 1017002, 网络出版: 2019-05-09
基于分组主成分分析的光学相干图像降斑算法 
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Optical Coherent Image Despeckling Algorithm Based on Grouping Principal Component Analysis
图 & 表
图 2. 仿真图像的实验结果。(a)原始指纹图像;(b)加入视数L=2的斑点噪声图像;(c) WST滤波后;(d) MSAR滤波后;(e) NLM滤波后;(f)所提算法滤波后
Fig. 2. Experimental results of simulated image. (a) Original fingerprint image; (b) noisy image corrupted by two-look speckle; (c) image after filtering with WST; (d) image after filtering with MSAR; (e) image after filtering with NLM; (f) image after filtering with proposed algorithm
图 3. 3幅人眼眼底组织的OCT图像(蓝色框区域用于计算SNR,红色框区域用于计算对比噪声比)。(a)图像1;(b)图像2;(c)图像3
Fig. 3. Three OCT images of human ocular fundus tissue (blue boxes are used to calculate SNR values, while the red boxes are used to obtain CNR values). (a) Image 1; (b) image 2; (c) image 3
图 4. 图像1的降斑结果。(a) WST滤波后;(b) MSAR滤波后;(c) NLM滤波后;(d)所提算法滤波后
Fig. 4. Despeckling results of image 1. (a) Image after filtering with WST; (b) image after filtering with MSAR; (c) image after filtering with NLM; (d) image after filtering with proposed algorithm
图 5. 图像2的降斑结果。(a) WST滤波后;(b) MSAR滤波后;(c) NLM滤波后;(d)所提算法滤波后
Fig. 5. Despeckling results of image 2. (a) Image after filtering with WST; (b) image after filtering with MSAR; (c) image after filtering with NLM; (d)image after filtering with proposed algorithm
表 1仿真图像降斑后的PSNR和SSIM
Table1. PSNR and SSIM for simulated images after despeckling
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表 2图像1、图像2、图像3降斑后的SNR、CNR和ENL平均值
Table2. Average values of SNR, CNR and ENL for image1, image 2 and image 3
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方敬, 滕树云, 牛四杰, 李登旺. 基于分组主成分分析的光学相干图像降斑算法[J]. 光学学报, 2018, 38(10): 1017002. Jing Fang, Shuyun Teng, Sijie Niu, Dengwang Li. Optical Coherent Image Despeckling Algorithm Based on Grouping Principal Component Analysis[J]. Acta Optica Sinica, 2018, 38(10): 1017002.
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