Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images
Source abstract
Abstract
Noise and blurring in light microscope images are representative factors that affect accurate identification of cellular and subcellular structures in biological research. In this study, a method for l1-norm-based blind deconvolution after noise reduction with non-subsampled contourlet transform (NSCT) was designed and applied to a light microscope image to analyze its feasibility. The designed NSCT-based algorithm first separated the low- and high-frequency components. Then, the restored microscope image and the deblurred and denoised images were compared and evaluated. In both the simulations and experiments, the average coefficient of variation (COV) value in the image using the proposed NSCT-based algorithm showed similar values compared to the denoised image; moreover, it significantly improved the results compared with that of the degraded image. In particular, we confirmed that the restored image in the experiment improved the COV by approximately 2.52 times compared with the deblurred image, and the NSCT-based proposed algorithm showed the best performance in both the peak signal-to-noise ratio and edge preservation index in the simulation. In conclusion, the proposed algorithm was successfully modeled, and the applicability of the proposed method in light microscope images was proved based on various quantitative evaluation indices.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image Fusion Techniques · Image and Signal Denoising Methods · Image Processing Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biomedical Engineering, Eulji University, 553 Sanseong-daero, Sujeong-gu, Seongnam-si 13135, Republic of Korea
Department of Dental Hygiene, Gachon University, 191 Hambakmoero, Yeonsu-gu, Incheon 21936, Republic of Korea
Funding
National Research Foundation of Korea (NRF)
References (64)
Show source-provided references
1. On-the-fly estimation of a microscopy point spread function10.1364/OE.26.026120 ↗
2. Measuring and interpreting point spread functions to determine confocal microscope resolution and ensure quality control10.1038/nprot.2011.407 ↗
3. Deconvolution methods for 3-D fluorescence microscopy images10.1109/MSP.2006.1628876 ↗
4. Born, M., and Wolf, E. (2020). Principles of Optics: 60th Anniversary Edition, Cambridge University Press. [7th ed.].10.1017/9781108769914 ↗
5. 3-D PSF fitting for fluorescence microscopy: Implementation and localization application10.1111/j.1365-2818.2012.03675.x ↗
6. Wiener, N. (1964). Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications, The MIT Press.
7. A survey on variational characterizations for nonlinear eigenvalue problems10.1553/etna_vol55s1 ↗
8. An Iteration Formula for Fredholm Integral Equations of the First Kind10.2307/2372313 ↗
9. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems10.1137/080716542 ↗
10. Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution10.1002/jemt.20294 ↗
11. An iterative technique for the rectification of observed distributions10.1086/111605 ↗
12. Bayesian-Based Iterative Method of Image Restoration*10.1364/JOSA.62.000055 ↗
13. AIDA: An adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data10.1364/JOSAA.24.001580 ↗
14. DeconvolutionLab2: An open-source software for deconvolution microscopy10.1016/j.ymeth.2016.12.015 ↗
15. Blind deconvolution through digital signal processing10.1109/PROC.1975.9800 ↗
16. Blind deconvolution of spatially invariant image blurs with phase10.1109/TASSP.1976.1162770 ↗
17. Linear and Nonlinear Image Deblurring: A Documented Study10.1137/S0036142997320413 ↗
18. Blind image deconvolution10.1109/79.489268 ↗
19. Blind deconvolution of quantum-limited incoherent imagery: Maximum-likelihood approach10.1364/JOSAA.9.001052 ↗
20. Blind deconvolution of fluorescence micrographs by maximum-likelihood estimation10.1364/AO.34.006633 ↗
21. Projection-based blind deconvolution10.1364/JOSAA.11.002401 ↗
22. Kernel PCA and De-Noising in Feature Spaces
23. Blind image deconvolution using machine learning for three-dimensional microscopy10.1109/TPAMI.2010.45 ↗
24. Denoising of Microscopy Images: A Review of the State-of-the-Art, and a New Sparsity-Based Method10.1109/TIP.2018.2819821 ↗
25. Iterative, constrained 3-D image reconstruction of transmitted light bright-field micrographs based on maximum likelihood estimation10.1111/j.1365-2818.1993.tb03311.x ↗
26. Alignment maps of tissues: I
27. Sparse denoising and adaptive estimation enhances the resolution and contrast of fluorescence emission difference microscopy based on an array detector10.1364/OL.474883 ↗
28. High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network10.1364/BOE.10.001044 ↗
29. Background estimation in nonlinear image restoration10.1364/JOSAA.17.000425 ↗
30. Chaux, C., Blanc-Féraud, L., and Zerubia, J. (2007). Wavelet-Based Restoration Methods: Application to 3D Confocal Microscopy Images, SPIE.10.1117/12.731438 ↗
31. Wavelet analysis for single molecule localization microscopy10.1364/OE.20.002081 ↗
32. Blind deblurring and denoising via a learning deep CNN denoiser prior and an adaptive L0-regularised gradient prior for passive millimetre-wave images10.1049/iet-ipr.2020.1193 ↗
33. HINRDNet: A half instance normalization residual dense network for passive millimetre wave image restoration10.1016/j.infrared.2023.104722 ↗
34. The Nonsubsampled Contourlet Transform: Theory, Design, and Applications10.1109/TIP.2006.877507 ↗
35. The contourlet transform: An efficient directional multiresolution image representation10.1109/TIP.2005.859376 ↗
36. The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density10.1155/2015/262819 ↗
37. A filter bank for the directional decomposition of images: Theory and design10.1109/78.127960 ↗
38. Diwakar, M., and Kumar, M. (2016, January 22–24). Edge preservation based CT image denoising using Wiener filtering and thresholding in wavelet domain. Proceedings of the 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India.10.1109/PDGC.2016.7913171 ↗
39. Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra10.1038/srep08017 ↗
40. Optimization of mask size for median-modified Wiener filter according to matrix size of computed tomography images10.1016/j.nima.2021.165508 ↗
41. Amer, A., Mitiche, A., and Dubois, E. (2002, January 22–25). Reliable and fast structure-oriented video noise estimation. Proceedings of the Proceedings. International Conference on Image Processing, Rochester, NY, USA.
42. Precision of light intensity measurement in biological optical microscopy10.1111/j.1365-2818.2007.01764.x ↗
43. Method of calibration of a fluorescence microscope for quantitative studies10.1111/j.1365-2818.2011.03514.x ↗
44. Estimation of the Noise Level Function Based on a Nonparametric Detection of Homogeneous Image Regions10.1137/15M1012682 ↗
45. The Treatment of Ties in Ranking Problems10.1093/biomet/33.3.239 ↗
46. A New Measure of Rank Correlation10.1093/biomet/30.1-2.81 ↗
47. A blind-deblurring method based on a compressed-sensing scheme in digital breast tomosynthesis10.1016/j.optlaseng.2018.06.011 ↗
48. Chan, S.H., Khoshabeh, R., Gibson, K.B., Gill, P.E., and Nguyen, T.Q. (2011, January 22–27). An augmented Lagrangian method for video restoration. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.10.1109/ICASSP.2011.5946560 ↗
49. Application of Blind Deconvolution Based on the New Weighted L(1)-norm Regularization with Alternating Direction Method of Multipliers in Light Microscopy Images10.1017/S143192762000183X ↗
50. Image enhancement based on a nonlinear multiscale method10.1109/83.585239 ↗
51. No-reference image quality assessment in the spatial domain10.1109/TIP.2012.2214050 ↗
52. Making a “Completely Blind” Image Quality Analyzer10.1109/LSP.2012.2227726 ↗
53. Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index10.1109/TIP.2013.2293423 ↗
54. Krishnan, D., Tay, T., and Fergus, R. (2011, January 20–25). Blind deconvolution using a normalized sparsity measure. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.10.1109/CVPR.2011.5995521 ↗
55. Xu, L., Zheng, S., and Jia, J. (2013, January 23–28). Unnatural L0 Sparse Representation for Natural Image Deblurring. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.10.1109/CVPR.2013.147 ↗
56. Krishnan, D., Bruna, J., and Fergus, R. (2013). Blind Deconvolution with Non-local Sparsity Reweighting. arXiv.
57. Investigation of a blind-deconvolution framework after noise reduction using a gamma camera in nuclear medicine imaging10.1016/j.net.2020.04.032 ↗
58. An Anisotropic Fourth-Order Diffusion Filter for Image Noise Removal10.1007/s11263-010-0330-1 ↗
59. Kim, K., and Kim, J.Y. (2021). Blind Deconvolution Based on Compressed Sensing with bi-I0-I2-norm Regularization in Light Microscopy Image. Int. J. Environ. Res. Public. Health, 18.10.3390/ijerph18041789 ↗
60. Nasonov, A., and Krylov, A. (2018, January 26–28). An Improvement of BM3D Image Denoising and Deblurring Algorithm by Generalized Total Variation. Proceedings of the 2018 7th European Workshop on Visual Information Processing (EUVIP), Tampere, Finland.10.1109/EUVIP.2018.8611693 ↗
61. On the mathematical properties of the structural similarity index10.1109/TIP.2011.2173206 ↗
62. The Potential of Light Microscopic Features of the Oral Mucosa in Predicting Post-mortem Interval10.18295/squmj.2021.21.01.005 ↗
63. Rakhshanfar, M., and Amer, M.A. (2018, January 7–10). Low-frequency image noise removal using white noise filter. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.10.1109/ICIP.2018.8451391 ↗
64. Image denoising in acoustic microscopy using block-matching and 4D filter10.1038/s41598-023-40301-7 ↗