Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model

• URL: http://arxiv.org/abs/2201.11760v1
• Date: Thu, 27 Jan 2022 19:02:38 GMT
• Title: Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model
• Authors: Dewei Hu, Yuankai K. Tao and Ipek Oguz
• Abstract summary: We present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal. Our method can significantly improve the image quality with a simple working pipeline and a small amount of training data.
• Score: 0.2578242050187029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical coherence tomography (OCT) is a prevalent non-invasive imaging method which provides high resolution volumetric visualization of retina. However, its inherent defect, the speckle noise, can seriously deteriorate the tissue visibility in OCT. Deep learning based approaches have been widely used for image restoration, but most of these require a noise-free reference image for supervision. In this study, we present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal. A diffusion process is defined by adding a sequence of Gaussian noise to self-fused OCT b-scans. Then the reverse process of diffusion, modeled by a Markov chain, provides an adjustable level of denoising. Our experiment results demonstrate that our method can significantly improve the image quality with a simple working pipeline and a small amount of training data.

Related papers

• Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging [32.99597899937902]
  We propose a novel unsupervised anomaly detection framework based on a diffusion model. The proposed framework incorporates a synthetic anomaly (Synomaly) noise function and a multi-stage diffusion process. We validate the proposed approach on carotid US, brain MRI, and liver CT datasets.
  arXiv  Detail & Related papers  (2024-11-06T15:43:51Z)
• Ultrasound Imaging based on the Variance of a Diffusion Restoration Model [7.360352432782388]
  We propose a hybrid reconstruction method combining an ultrasound linear direct model with a learning-based prior coming from a generative Denoising Diffusion model. We conduct experiments on synthetic, in-vitro, and in-vivo data, demonstrating the efficacy of our variance imaging approach in achieving high-quality image reconstructions.
  arXiv  Detail & Related papers  (2024-03-22T16:10:38Z)
• Deep Ultrasound Denoising Using Diffusion Probabilistic Models [5.828784149537374]
  Previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound. Herein, a method based on the recent Denoising Diffusion Probabilistic Models (DDPM) is proposed. It iteratively enhances the image quality by eliminating the noise while preserving the speckle texture.
  arXiv  Detail & Related papers  (2023-06-12T21:53:32Z)
• Masked Image Training for Generalizable Deep Image Denoising [53.03126421917465]
  We present a novel approach to enhance the generalization performance of denoising networks. Our method involves masking random pixels of the input image and reconstructing the missing information during training. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios.
  arXiv  Detail & Related papers  (2023-03-23T09:33:44Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• SAR Despeckling using a Denoising Diffusion Probabilistic Model [52.25981472415249]
  The presence of speckle degrades the image quality and adversely affects the performance of SAR image understanding applications. We introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method achieves significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods.
  arXiv  Detail & Related papers  (2022-06-09T14:00:26Z)
• MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion [38.62448918459113]
  We propose a new denoising method based on score-based reverse diffusion sampling. Our network, trained only with coronal knee scans, excels even on out-of-distribution in vivo liver MRI data.
  arXiv  Detail & Related papers  (2022-03-23T10:35:06Z)
• Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising [20.965610734723636]
  Deep learning-based technique has emerged as a dominant method for low dose CT(LDCT) denoising. In this study, we propose a novel convolutional module as the first attempt to utilize neighborhood similarity of CT images for denoising tasks. Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising.
  arXiv  Detail & Related papers  (2020-11-11T10:44:52Z)
• Unpaired Learning of Deep Image Denoising [80.34135728841382]
  This paper presents a two-stage scheme by incorporating self-supervised learning and knowledge distillation. For self-supervised learning, we suggest a dilated blind-spot network (D-BSN) to learn denoising solely from real noisy images. Experiments show that our unpaired learning method performs favorably on both synthetic noisy images and real-world noisy photographs.
  arXiv  Detail & Related papers  (2020-08-31T16:22:40Z)
• Improving Blind Spot Denoising for Microscopy [73.94017852757413]
  We present a novel way to improve the quality of self-supervised denoising. We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network.
  arXiv  Detail & Related papers  (2020-08-19T13:06:24Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.