OCT image denoising based on Bayesian non-local mean filter and deep learning network

Haotian Liu

doi:10.1117/12.2681319

8 June 2023 OCT image denoising based on Bayesian non-local mean filter and deep learning network

Haotian Liu

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127070G (2023) https://doi.org/10.1117/12.2681319
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Optical Coherence Tomography (OCT) uses low coherence light to provide a high spatial resolution to detect changes in the microstructure of living organisms in a non-invasive, real-time manner. A new OCT image denoising method is proposed to address the problem of poor noise reduction by conventional OCT image denoising algorithms. The method combines a Bayesian non-local mean filtering algorithm and deep learning to denoise noisy images for better noise reduction. By comparing with the Gaussian filtering algorithm, the median filtering algorithm, the BNLM (Bayesian nonlocal mean) denoising algorithm, the BM3D (block-matching and 3D filtering) denoising algorithm, the new algorithm outperforms the traditional method in terms of noise reduction. And compares the peak signal-to-noise ratio, structural similarity and other metrics. New algorithm shows superiority.

Citation Download Citation

Haotian Liu "OCT image denoising based on Bayesian non-local mean filter and deep learning network", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127070G (8 June 2023); https://doi.org/10.1117/12.2681319

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