Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910343024869376 |
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| author | Özkan, Aytaç Stoykova, Elena Sikora, Thomas Madjarova, Violeta |
| author_facet | Özkan, Aytaç Stoykova, Elena Sikora, Thomas Madjarova, Violeta |
| contents | In Optical Coherence Tomography (OCT), speckle noise significantly hampers image quality, affecting diagnostic accuracy. Current methods, including traditional filtering and deep learning techniques, have limitations in noise reduction and detail preservation. Addressing these challenges, this study introduces a novel denoising algorithm, Block-Matching Steered-Mixture of Experts with Multi-Model Inference and Autoencoder (BM-SMoE-AE). This method combines block-matched implementation of the SMoE algorithm with an enhanced autoencoder architecture, offering efficient speckle noise reduction while retaining critical image details. Our method stands out by providing improved edge definition and reduced processing time. Comparative analysis with existing denoising techniques demonstrates the superior performance of BM-SMoE-AE in maintaining image integrity and enhancing OCT image usability for medical diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12735 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference Özkan, Aytaç Stoykova, Elena Sikora, Thomas Madjarova, Violeta Image and Video Processing Computer Vision and Pattern Recognition In Optical Coherence Tomography (OCT), speckle noise significantly hampers image quality, affecting diagnostic accuracy. Current methods, including traditional filtering and deep learning techniques, have limitations in noise reduction and detail preservation. Addressing these challenges, this study introduces a novel denoising algorithm, Block-Matching Steered-Mixture of Experts with Multi-Model Inference and Autoencoder (BM-SMoE-AE). This method combines block-matched implementation of the SMoE algorithm with an enhanced autoencoder architecture, offering efficient speckle noise reduction while retaining critical image details. Our method stands out by providing improved edge definition and reduced processing time. Comparative analysis with existing denoising techniques demonstrates the superior performance of BM-SMoE-AE in maintaining image integrity and enhancing OCT image usability for medical diagnostics. |
| title | Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.12735 |