Physics-Based Practical Speckle Noise Modeling for Optical Coherence Tomography Image Denoising
Abstract
:1. Introduction
- We attempted to address the problem of OCT image denoising using a noise synthesis-based approach combined with deep learning. Our method successfully generated noise images that closely resembled the distribution of real noise while preserving the noise patterns consistent with the original clean images.
- In the process of designing the denoising algorithm, we took into full consideration the physical characteristics of OCT and incorporated speckle simulation algorithms into the noise synthesizer. This innovative approach of combining speckle simulation with deep learning represents the first of its kind in current OCT denoising research.
- We designed an innovative dual-module noise generator to specifically address speckle noise and device noise present in practical speckle noise.
- Extensive experiments have shown that our proposed method has achieved state-of-the-art performance in the field of unpaired OCT image denoising, demonstrating its powerful ability to preserve structural details. Additionally, through ablation experiments, the importance of incorporating OCT physical information for improving denoising results has been further confirmed.
2. Materials and Methods
2.1. Method Background
2.2. Train the Noise Generator Using Unpaired Images
2.3. The Noise Generator
Algorithm 1 Speckle Simulation |
|
2.4. Simulation of Speckle Image
2.5. Train the Denoising Model Using Paired Images
3. Results
3.1. Experimental Preparation
3.2. Comparison of Synthesized Noise with Other Noise Synthesis Models
3.3. Comparison of Denoising Results with Other OCT Denoising Methods
3.4. Ablation Experiments
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Noisy | BM3D | NLM | DRGAN | TCFL | AWGN | C2N | OURS | |
---|---|---|---|---|---|---|---|---|
PSNR | 19.026 ± 0.093 | 33.762 ± 0.216 | 26.404 ± 0.244 | 28.686 ± 1.834 | 30.523 ± 0.753 | 34.340 ± 0.474 | 35.828 ± 0.467 | 38.504 ± 0.590 |
SSIM | 0.122 ± 0.011 | 0.858 ± 0.004 | 0.437 ± 0.008 | 0.863 ± 0.043 | 0.875 ± 0.086 | 0.902 ± 0.011 | 0.928 ± 0.004 | 0.954 ± 0.002 |
GCMSE | 18.233 ± 4.578 | 2.993 ± 1.969 | 6.567 ± 2.794 | 23.507 ± 5.214 | 5.810 ± 2.532 | 1.199 ± 1.169 | 1.318 ± 1.280 | 0.899 ± 1.046 |
Noisy | BM3D | NLM | DRGAN | TCFL | OURS | |
---|---|---|---|---|---|---|
PSNR | 20.157 ± 0.383 | 29.259 ± 0.560 | 24.455 ± 0.325 | 26.934 ± 0.641 | 29.418 ± 0.539 | 30.490 ± 0.993 |
SSIM | 0.172 ± 0.021 | 0.557 ± 0.024 | 0.325 ± 0.025 | 0.569 ± 0.014 | 0.635 ± 0.026 | 0.749 ± 0.023 |
GCMSE | 33.410 ± 6.702 | 15.709 ± 3.513 | 14.80 ± 3.854 | 14.832 ± 3.634 | 7.723 ± 3.850 | 6.922 ± 2.854 |
Speckle-I | Speckle-D | PSNR | SSIM |
---|---|---|---|
× | × | 35.828 ± 0.467 | 0.928 ± 0.039 |
✓ | × | 36.230 ± 0.482 | 0.945 ± 0.017 |
✓ | ✓ | 38.504 ± 0.590 | 0.954 ± 0.002 |
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Yang, L.; Wu, D.; Gao, W.; Xu, R.X.; Sun, M. Physics-Based Practical Speckle Noise Modeling for Optical Coherence Tomography Image Denoising. Photonics 2024, 11, 569. https://doi.org/10.3390/photonics11060569
Yang L, Wu D, Gao W, Xu RX, Sun M. Physics-Based Practical Speckle Noise Modeling for Optical Coherence Tomography Image Denoising. Photonics. 2024; 11(6):569. https://doi.org/10.3390/photonics11060569
Chicago/Turabian StyleYang, Lei, Di Wu, Wenteng Gao, Ronald X. Xu, and Mingzhai Sun. 2024. "Physics-Based Practical Speckle Noise Modeling for Optical Coherence Tomography Image Denoising" Photonics 11, no. 6: 569. https://doi.org/10.3390/photonics11060569
APA StyleYang, L., Wu, D., Gao, W., Xu, R. X., & Sun, M. (2024). Physics-Based Practical Speckle Noise Modeling for Optical Coherence Tomography Image Denoising. Photonics, 11(6), 569. https://doi.org/10.3390/photonics11060569