Denoising in SVD-based ghost imaging
By the method of singular-valued decomposition (SVD), ghost imaging (GI) reconstructs the images with high efficiency. However, a small amount of noise can greatly degrade or even destroy the object information. In this paper, we experimentally investigate the method of truncated SVD (TSVD) by selec...
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| Veröffentlicht in: | Optics express 2022-02, Vol.30 (4), p.6248-6257 |
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| container_title | Optics express |
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| creator | Chen, Liu-Ya Wang, Chong Xiao, Xu-Yi Ren, Cheng Zhang, De-Jian Li, Zhuan Cao, De-Zhong |
| description | By the method of singular-valued decomposition (SVD), ghost imaging (GI) reconstructs the images with high efficiency. However, a small amount of noise can greatly degrade or even destroy the object information. In this paper, we experimentally investigate the method of truncated SVD (TSVD) by selecting the first few largest singular values to enhance the image quality. The contrast-to-noise ratio and structural similarity of the images are improved with appropriate truncation ratios. To further improve the image quality, we analyze the noise effects on TSVD-based GI and introduce additional filters. TSVD-based GI may find its applications in rapid imaging under complicated environment conditions. |
| doi_str_mv | 10.1364/OE.452991 |
| format | Article |
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| title | Denoising in SVD-based ghost imaging |
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