A variational network for biomedical images denoising using bayesian model and auto-encoder

Auto-encoders have demonstrated outstanding performance in computer vision tasks such as biomedical imaging, including classification, segmentation, and denoising. Many of the current techniques for image denoising in biomedical applications involve training an autoencoder or convolutional neural network (CNN) using pairs of clean and noisy images. However, these approaches are not realistic because the autoencoder or CNN is trained on known noise and does not generalize well to new noisy distributions. This paper proposes a novel approach for biomedical image denoising using a variational network based on a Bayesian model and deep learning.
Method: In this study, we aim to denoise biomedical images using a Bayesian approach. In our dataset, each image exhibited a same noise distribution. To achieve this, we first estimate the noise distribution based on Bayesian probability by calculating the posterior distributions, and then proceed with denoising. A loss function that combines the Bayesian prior and autoencoder objectives is used to train the variational network. The proposed method was tested on CT-Scan biomedical image datasets and compared with state-of-the-art denoising techniques.
Results: The experimental results demonstrate that our method outperforms the existing methods in terms of denoising accuracy, visual quality, and computational efficiency. For instance, we obtained a PSNR of 39.18 dB and an SSIM of 0.9941 with noise intensity std = 10. Our approach can potentially improve the accuracy and reliability of biomedical image analysis, which can have significant implications for clinical diagnosis and treatment planning.
Conclusion: The proposed method combines the advantages of both Bayesian modeling and variational network to effectively denoise biomedical images.&#xD.