Noise2Inverse: Self-supervised deep convolutional denoising for
tomography
- URL: http://arxiv.org/abs/2001.11801v3
- Date: Tue, 15 Sep 2020 08:27:07 GMT
- Title: Noise2Inverse: Self-supervised deep convolutional denoising for
tomography
- Authors: Allard A. Hendriksen, Daniel M. Pelt and K. Joost Batenburg
- Abstract summary: Noise2Inverse is a deep CNN-based denoising method for linear image reconstruction algorithms.
We develop a theoretical framework which shows that such training indeed obtains a denoising CNN.
On simulated CT datasets, Noise2Inverse demonstrates an improvement in peak signal-to-noise ratio and structural similarity index.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recovering a high-quality image from noisy indirect measurements is an
important problem with many applications. For such inverse problems, supervised
deep convolutional neural network (CNN)-based denoising methods have shown
strong results, but the success of these supervised methods critically depends
on the availability of a high-quality training dataset of similar measurements.
For image denoising, methods are available that enable training without a
separate training dataset by assuming that the noise in two different pixels is
uncorrelated. However, this assumption does not hold for inverse problems,
resulting in artifacts in the denoised images produced by existing methods.
Here, we propose Noise2Inverse, a deep CNN-based denoising method for linear
image reconstruction algorithms that does not require any additional clean or
noisy data. Training a CNN-based denoiser is enabled by exploiting the noise
model to compute multiple statistically independent reconstructions. We develop
a theoretical framework which shows that such training indeed obtains a
denoising CNN, assuming the measured noise is element-wise independent and
zero-mean. On simulated CT datasets, Noise2Inverse demonstrates an improvement
in peak signal-to-noise ratio and structural similarity index compared to
state-of-the-art image denoising methods and conventional reconstruction
methods, such as Total-Variation Minimization. We also demonstrate that the
method is able to significantly reduce noise in challenging real-world
experimental datasets.
Related papers
- Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising [19.08732222562782]
Supervised deep learning has become the method of choice for image denoising.
We show that, contrary to popular belief, denoising networks specialized in the removal of Gaussian noise can be efficiently leveraged in favor of real-world image denoising.
arXiv Detail & Related papers (2024-07-24T16:23:46Z) - Enhancing convolutional neural network generalizability via low-rank weight approximation [6.763245393373041]
Sufficient denoising is often an important first step for image processing.
Deep neural networks (DNNs) have been widely used for image denoising.
We introduce a new self-supervised framework for image denoising based on the Tucker low-rank tensor approximation.
arXiv Detail & Related papers (2022-09-26T14:11:05Z) - Robust Deep Ensemble Method for Real-world Image Denoising [62.099271330458066]
We propose a simple yet effective Bayesian deep ensemble (BDE) method for real-world image denoising.
Our BDE achieves +0.28dB PSNR gain over the state-of-the-art denoising method.
Our BDE can be extended to other image restoration tasks, and achieves +0.30dB, +0.18dB and +0.12dB PSNR gains on benchmark datasets.
arXiv Detail & Related papers (2022-06-08T06:19:30Z) - Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image [34.27748767631027]
We present a novel self-supervised learning method for single-image denoising.
We approximate traditional iterative optimization algorithms for image denoising with a recurrent neural network.
Our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM.
arXiv Detail & Related papers (2022-06-04T00:08:58Z) - Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs)
We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
arXiv Detail & Related papers (2022-04-05T12:46:36Z) - IDR: Self-Supervised Image Denoising via Iterative Data Refinement [66.5510583957863]
We present a practical unsupervised image denoising method to achieve state-of-the-art denoising performance.
Our method only requires single noisy images and a noise model, which is easily accessible in practical raw image denoising.
To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes.
arXiv Detail & Related papers (2021-11-29T07:22:53Z) - Image Denoising using Attention-Residual Convolutional Neural Networks [0.0]
We propose a new learning-based non-blind denoising technique named Attention Residual Convolutional Neural Network (ARCNN) and its extension to blind denoising named Flexible Attention Residual Convolutional Neural Network (FARCNN)
ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for Gaussian and Poisson denoising, respectively FARCNN presented very consistent results, even with slightly worsen performance compared to ARCNN.
arXiv Detail & Related papers (2021-01-19T16:37:57Z) - Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
We present Neighbor2Neighbor to train an effective image denoising model with only noisy images.
In detail, input and target used to train a network are images sub-sampled from the same noisy image.
A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
arXiv Detail & Related papers (2021-01-08T02:03:25Z) - 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) - 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.