• Chinese Journal of Lasers
  • Vol. 48, Issue 13, 1307001 (2021)
Tianqi Wu1、2, Wen Xiao1、2, Renjian Li1、2, Yizhi Xu1、2, Xuejuan Hu2、3, and Lingling Chen1、2、3、*
Author Affiliations
  • 1College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, Guangdong 518118, China
  • 2College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
  • 3Key Laboratory of Advanced Optical Precision Manufacturing Technology of Guangdong Provincial Higher Education Institute, Shenzhen, Guangdong 518118, China
  • show less
    DOI: 10.3788/CJL202148.1307001 Cite this Article Set citation alerts
    Tianqi Wu, Wen Xiao, Renjian Li, Yizhi Xu, Xuejuan Hu, Lingling Chen. Single-Molecule Localization Image Background Denoising Based on Time-Domain Iterative Wavelet Transform[J]. Chinese Journal of Lasers, 2021, 48(13): 1307001 Copy Citation Text show less
    Flow chart of time-domain iterative wavelet transform algorithm
    Fig. 1. Flow chart of time-domain iterative wavelet transform algorithm
    Diagrams of SSIM and time consumption changing with levels and iterations under different SNR datasets. (a)--(b) Under low and middle SNR datasets; (c)--(d) under high SNR datasets
    Fig. 2. Diagrams of SSIM and time consumption changing with levels and iterations under different SNR datasets. (a)--(b) Under low and middle SNR datasets; (c)--(d) under high SNR datasets
    Different simulated SNR datasets and separated signals after processing with different algorithms. (a) Simulated SNR10 dataset; (b) separated signals with different algorithms for SNR10 dataset; (c) simulated SNR6 dataset; (d) separated signals with different algorithms for SNR6 dataset; (e) simulated SNR2 dataset; (f) separated signals with different algorithms for SNR2 dataset; (g) normalized intensity of single fluorescence signal
    Fig. 3. Different simulated SNR datasets and separated signals after processing with different algorithms. (a) Simulated SNR10 dataset; (b) separated signals with different algorithms for SNR10 dataset; (c) simulated SNR6 dataset; (d) separated signals with different algorithms for SNR6 dataset; (e) simulated SNR2 dataset; (f) separated signals with different algorithms for SNR2 dataset; (g) normalized intensity of single fluorescence signal
    Comparison of SSIM and PSNR under different algorithms in simulated datasets with high, middle and low SNRs and time consumption of different algorithms. (a) Comparison of SSIM and PSNR under different algorithms in SNR10 datasets; (b) comparison of SSIM and PSNR under different algorithms in SNR6 datasets; (c) comparison of SSIM and PSNR under different algorithms in SNR2 datasets; (d) time consumption of different algorithms
    Fig. 4. Comparison of SSIM and PSNR under different algorithms in simulated datasets with high, middle and low SNRs and time consumption of different algorithms. (a) Comparison of SSIM and PSNR under different algorithms in SNR10 datasets; (b) comparison of SSIM and PSNR under different algorithms in SNR6 datasets; (c) comparison of SSIM and PSNR under different algorithms in SNR2 datasets; (d) time consumption of different algorithms
    Reconstructed super-resolved images of Tubulin ConjAL647. (a)--(f) Images reconstructed with TDIWT, EVER, GF, BEWT, RB, and MF algorithms, respectively; (g) raw dataset
    Fig. 5. Reconstructed super-resolved images of Tubulin ConjAL647. (a)--(f) Images reconstructed with TDIWT, EVER, GF, BEWT, RB, and MF algorithms, respectively; (g) raw dataset
    Three-dimensional experimental device diagram of easySTORM. (a) Three-dimensional diagram; (b) schematic
    Fig. 6. Three-dimensional experimental device diagram of easySTORM. (a) Three-dimensional diagram; (b) schematic
    Super-resolved images reconstructed with different algorithms. (a)--(f) Super-resolved images reconstructed with TDIWT, EVER, GF, BEWT, RB, and MF pre-processing; (g) bright-field fluorescence image; (h) structural noise in dashed box
    Fig. 7. Super-resolved images reconstructed with different algorithms. (a)--(f) Super-resolved images reconstructed with TDIWT, EVER, GF, BEWT, RB, and MF pre-processing; (g) bright-field fluorescence image; (h) structural noise in dashed box
    Tianqi Wu, Wen Xiao, Renjian Li, Yizhi Xu, Xuejuan Hu, Lingling Chen. Single-Molecule Localization Image Background Denoising Based on Time-Domain Iterative Wavelet Transform[J]. Chinese Journal of Lasers, 2021, 48(13): 1307001
    Download Citation