Speckle Image Restoration without Clean Data
- URL: http://arxiv.org/abs/2205.08833v1
- Date: Wed, 18 May 2022 10:06:17 GMT
- Title: Speckle Image Restoration without Clean Data
- Authors: Tsung-Ming Tai, Yun-Jie Jhang, Wen-Jyi Hwang, Chau-Jern Cheng
- Abstract summary: We propose a novel image restoration algorithm that can perform speckle noise removal without clean data.
Our method even shows promising results across different speckle noise strengths, without the clean data needed.
- Score: 0.18528576113792794
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Speckle noise is an inherent disturbance in coherent imaging systems such as
digital holography, synthetic aperture radar, optical coherence tomography, or
ultrasound systems. These systems usually produce only single observation per
view angle of the same interest object, imposing the difficulty to leverage the
statistic among observations. We propose a novel image restoration algorithm
that can perform speckle noise removal without clean data and does not require
multiple noisy observations in the same view angle. Our proposed method can
also be applied to the situation without knowing the noise distribution as
prior. We demonstrate our method is especially well-suited for spectral images
by first validating on the synthetic dataset, and also applied on real-world
digital holography samples. The results are superior in both quantitative
measurement and visual inspection compared to several widely applied baselines.
Our method even shows promising results across different speckle noise
strengths, without the clean data needed.
Related papers
- Explainable Synthetic Image Detection through Diffusion Timestep Ensembling [30.298198387824275]
Recent advances in diffusion models have enabled the creation of deceptively real images.
Recent advances in diffusion models have enabled the creation of deceptively real images, posing significant security risks when misused.
arXiv Detail & Related papers (2025-03-08T13:04:20Z) - Speckle Noise Reduction in Ultrasound Images using Denoising
Auto-encoder with Skip Connection [0.19116784879310028]
Ultrasound images often contain speckle noise which can lower their resolution and contrast-to-noise ratio.
This can make it more difficult to extract, recognize, and analyze features in the images.
Researchers have proposed several speckle reduction methods, but no single method takes all relevant factors into account.
arXiv Detail & Related papers (2024-03-05T08:08:59Z) - Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise [54.0185721303932]
We present the first fully unsupervised deep learning-based denoiser capable of handling imaging noise that is row-correlated.
Our approach uses a Variational Autoencoder with a specially designed autoregressive decoder.
Our method does not require a pre-trained noise model and can be trained from scratch using unpaired noisy data.
arXiv Detail & Related papers (2023-10-11T20:48:20Z) - 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) - 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) - Compressive Ptychography using Deep Image and Generative Priors [9.658250977094562]
Ptychography is a well-established coherent diffraction imaging technique that enables non-invasive imaging of samples at a nanometer scale.
One major limitation of ptychography is the long data acquisition time due to mechanical scanning of the sample.
We propose a generative model combining deep image priors with deep generative priors.
arXiv Detail & Related papers (2022-05-05T02:18:26Z) - Rethinking Noise Synthesis and Modeling in Raw Denoising [75.55136662685341]
We introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise.
It inherently generates accurate raw image noise for different camera sensors.
arXiv Detail & Related papers (2021-10-10T10:45:24Z) - Joint self-supervised blind denoising and noise estimation [0.0]
Two neural networks jointly predict the clean signal and infer the noise distribution.
We show empirically with synthetic noisy data that our model captures the noise distribution efficiently.
arXiv Detail & Related papers (2021-02-16T08:37:47Z) - Unsupervised Image Restoration Using Partially Linear Denoisers [2.3061446605472558]
We propose a class of structured denoisers that can be decomposed as the sum of a nonlinear image-dependent mapping, a linear noise-dependent term and a small residual term.
We show that these denoisers can be trained with only noisy images under the condition that the noise has zero mean and known variance.
Our method outperforms some recent unsupervised and self-supervised deep denoising models that do not require clean images for their training.
arXiv Detail & Related papers (2020-08-14T02:13:19Z) - Dual Adversarial Network: Toward Real-world Noise Removal and Noise
Generation [52.75909685172843]
Real-world image noise removal is a long-standing yet very challenging task in computer vision.
We propose a novel unified framework to deal with the noise removal and noise generation tasks.
Our method learns the joint distribution of the clean-noisy image pairs.
arXiv Detail & Related papers (2020-07-12T09:16:06Z) - Fully Unsupervised Diversity Denoising with Convolutional Variational
Autoencoders [81.30960319178725]
We propose DivNoising, a denoising approach based on fully convolutional variational autoencoders (VAEs)
First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder.
We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training.
arXiv Detail & Related papers (2020-06-10T21:28:13Z) - 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.