Improved low-dose CT images with deep learning and framelet denoising.
problem Low-dose X-ray CT images often lack texture and detail.
method Proposed a wavelet residual network combining deep learning and framelet denoising.
result Significantly improved performance in preserving image detail.
Graph neural networks are explained through energy gradient flow and framelet decomposition.
problem Understanding and improving graph neural networks.
method Viewing framelet-based models as gradient flows of energy, proposing a generalized energy via framelet decomposition.
result The proposed model leads to more flexible dynamics, enhancing graph neural networks.
Deep convolution framelets improve deep learning for inverse problems.
problem Improving deep learning performance in inverse problems.
method Developed convolution framelets for signal representation, combined with deep neural networks.
result Deep convolution framelets achieve perfect reconstruction (PR) and outperform existing architectures.
Paper introduces a method to process medical images efficiently.
problem High computational cost in processing large medical image data.
method Framelet-pooling aided deep learning method to reduce complexity.
result Significant reduction in computational costs with comparable performance.
Unified framework explains geometric properties of CNNs.
problem Understanding why encoder-decoder CNNs perform well.
method Unified mathematical framework based on recent neural network theories.
result Encoder-decoder CNNs are related to nonlinear basis representation using combinatorial convolution frames.
New deep learning methods improve CT image quality from few projections.
problem Sparse-view CT images suffer from streaking artifacts due to limited projections.
method Inspired by deep convolutional framelets, propose new U-Net variants that satisfy the frame condition.
result New U-Net variants provide better reconstruction performance for sparse-view CT.
Paper compares optimal denoising methods for generative models, finding different results based on data regularity.
problem Optimizing denoising in score-based generative models for various data types.
method Comparison of full-denoising and half-denoising approaches, analyzing performance in terms of distribution distances.
result Different denoising methods perform better under different data regularity conditions.
New method optimizes matrix denoising for weighted loss functions and heterogeneous signals.
problem Estimating low-rank matrices from noisy observed matrices.
method Developed a family of weighted loss functions and derived optimal spectral denoisers.
result A new denoiser exploiting heterogeneity in signal matrices improves estimation.
Unified method for simultaneous denoising and clustering.
problem Clustering noisy signals.
method Sparse convex wavelet clustering with fusion and group-sparse penalties.
result Unified approach that denoises and clusters simultaneously.
DDPD separates generation into planning and denoising for improved efficiency.
problem Efficiently denoise corrupted data during generation.
method Separates generation into a planner and denoiser, selecting denoising positions based on corruption severity.
result DDPD outperforms traditional methods on language and image generation benchmarks.
Bounded total variation denoising improves traffic analysis accuracy.
problem Improving traffic prediction and clustering accuracy in urban areas.
method Applying bounded total variation denoising to GPS traffic data and clustering analysis.
result Significant improvement in predicting and clustering accuracy after denoising.
Improved self-supervised denoising for Poisson-Gaussian noise.
problem Handling Poisson-Gaussian noise in self-supervised denoising.
method Extended blindspot model, improved training scheme without hyperparameters.
result Improved denoising performance on microscope image benchmarks.
Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however …
Total variation denoising improves image quality adaptively.
problem Improving image quality from noisy data.
method Total variation regularization for image denoising.
result Denoised images converge to true images at a parametric rate.
Gen-CUDE is a neural network for denoising noisy channels.
problem Denoising in finite-input, general-output noisy channels.
method Unsupervised neural network trained on noisy data.
result Gen-CUDE achieves better denoising results than other methods.
GDiff tackles blind denoising with Gibbs sampling and Monte Carlo inference.
problem Blind denoising of signals with unknown noise parameters.
method Gibbs Diffusion (GDiff) method that alternates sampling steps from a conditional diffusion model and a Monte Carlo sampler.
result GDiff achieves blind denoising of natural images and cosmic microwave background data.
SUNLayer framework improves image denoising stability.
problem Stable denoising of images and other inverse problems.
method Introduces SUNLayer framework based on spherical harmonics to analyze generative models.
result Demonstrates stable denoising performance of SUNLayer on various activation functions.
The paper analyzes Laplacian pyramids for extending and denoising discrete functions.
problem Analyzing conditions for convergence and stability of Laplacian pyramids.
method Investigates Laplacian pyramids for extension and denoising, providing convergence conditions and stability bounds.
result Mild conditions are provided under which the Laplacian pyramids algorithm converges and stability bounds are proven.
This paper tackles denoising of complex measures using optimal transport and curvature analysis.
problem Denoising of complex, possibly non-log-concave measures.
method Score function and optimal transport theory to revert Langevin diffusion chains.
result The difficulty of denoising depends on the curvature complexity of the initial measure at specific SNR scales.
Proves exact relationship between optimal denoising and data distribution.
problem Understanding the relationship between denoising and data distribution.
method Analyzes additive Gaussian noise to prove exact relationship.
result Generalizes known relationship to non-small noise conditions.
A new Dantzig Selector with an optimal denoising matrix for reinforcement learning.
problem Improving Dantzig Selector's performance in sparse signal recovery and reinforcement learning.
method Defining an optimal denoising matrix through minimax optimization and proposing an approximate algorithm to estimate it.
result Empirical validation of the proposed ODDS algorithm's superior performance in reinforcement learning.
New denoisers improve signal recovery from noisy data without knowing noise distribution.
problem Denoising signals when only noise level is known, not distribution.
method Universal denoisers that shrink PY toward PX with higher-order accuracy. result Achieves O(σ4) and O(σ6) accuracy in matching generalized moments and densities. A new dataset for denoising real fluorescence microscopy images.
problem Noisy real fluorescence microscopy images.
method Constructed a dataset of 12,000 real fluorescence microscopy images with different noise levels.
result Deep learning methods perform best on the denoising of the FMD dataset.
New model reduces sampling cost in diffusion models, making them faster and applicable to real-world applications.
problem Challenges in generating high-quality samples, mode coverage, and fast sampling in deep generative models.
method Proposes denoising diffusion GANs that model each denoising step using a multimodal conditional GAN to reduce sampling cost.
result Demonstrates 2000imes faster sampling on CIFAR-10 dataset while maintaining competitive sample quality and diversity. This paper improves neural machine translation training by selecting and denoising data.
problem Reduces negative impact of noisy data on neural machine translation training.
method Measures and selects domain data, applies denoising curriculum using online data selection.
result Significant effectiveness for training on noisy data.
Nyström approximation for scalable operator learning
problem Scalability of operator learning for large datasets
method Nyström subsampling with operator learning
result Minimax-optimal convergence rates for functional outputs
UDVD uses deep learning to denoise videos without supervision.
problem Lack of clean video data for training deep learning models.
method UDVD is a CNN trained solely on noisy video data, adapting to local motion.
result UDVD performs as well as supervised methods, even with limited training data.
Deep learning improves MRI image reconstruction from sparse k-space data.
problem Accelerated MRI imaging with limited k-space data.
method Data-driven deep learning using convolutional neural networks and Hankel matrix decomposition.
result Deep learning consistently outperforms existing image-domain methods in k-space MRI reconstruction.
Unified framework for denoising models across various spaces.
problem Improving generative models and approximate posterior simulation.
method Generalizing denoising diffusions to a broader class of spaces using a new extension of score matching.
result Unified models for denoising and posterior simulation.
Nonparametric empirical Bayes denoising on Riemannian manifolds
problem Denoising measurements on compact Riemannian manifolds
method Using a surrogate oracle denoiser based on the marginal distribution of measurements
result Achieving nearly the Bayes risk in a low-noise regime
This paper explains GNNs using graph signal denoising.
problem Understanding how GNNs work for node representation learning.
method Spectral graph convolutional networks and graph attention networks are analyzed from the perspective of graph signal denoising.
result GNNs implicitly solve graph signal denoising problems.
New method quantifies uncertainty in denoising models.
problem Uncertainty quantification in denoising models.
method Derives a relation between posterior moments and derivatives, uses it for efficient uncertainty quantification.
result Efficient computation of principal components and full marginal distributions of the posterior.
A new autoencoder framework transforms latent space to improve generative and denoising models.
problem Improving the performance of generative and denoising models in autoencoders.
method Homeomorphic transformation of latent variables to reduce distance and preserve topological properties.
result The transformed latent space leads to better performance in generative and denoising models, as measured by Hausdorff distance and visual characteristics.
New method improves image denoising with fewer parameters and less data.
problem Image denoising requires large datasets and supervised settings, limiting practical applications.
method Self-supervised framework using Tucker low-rank tensor approximation.
result Improves model generalizability and reduces data acquisition costs.
Physics-informed denoising improves sensor data accuracy without needing clean data.
problem Noise in real-life sensor data affects system performance and reliability.
method Physics-informed denoising model that uses algebraic relationships between sensor measurements governed by physical laws.
result Achieved state-of-the-art performance in various real-world applications.
We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet transform in order to per- form non-iterative denoising directly in the graph frequency domai…
FC-AIDE improves image denoising with a fully convolutional neural network.
problem Improving image denoising performance with neural networks.
method Fully convolutional neural network with adaptive fine-tuning.
result FC-AIDE outperforms state-of-the-art denoisers on benchmark datasets.
Image denoising based on a probabilistic model of local image patches has been employed by various researchers, and recently a deep (denoising) autoencoder has been proposed by Burger et al. [2012] and Xie et al. [2012] as a good model for this. In this paper, we propose that another popular family of models in the fie…
Noise2Inverse removes artifacts in noisy CT images without needing clean data.
problem Removing artifacts in noisy CT images.
method Noise2Inverse uses a deep CNN trained on multiple statistically independent reconstructions of the same noisy data.
result Noise2Inverse improves peak signal-to-noise ratio and structural similarity index compared to existing methods.
Paper analyzes self-supervised image denoising with denatured data.
problem Understanding the performance of self-supervised image denoising with denatured data.
method Theoretical analysis and numerical experiments on a denoising algorithm.
result Theoretical analysis shows the algorithm finds desired solutions to the optimization problem.
A denoising algorithm seeks to remove noise, errors, or perturbations from a signal. Extensive research has been devoted to this arena over the last several decades, and as a result, today's denoisers can effectively remove large amounts of additive white Gaussian noise. A compressed sensing (CS) reconstruction algorit…
New method denoises images without clean reference using Tweedie distributions.
problem Image denoising without clean reference images.
method Combining Tweedie distributions, Noise2Score, and saddle point approximation.
result General closed-form denoising formula for various noise distributions.
New method trains deep denoisers without ground truth data.
problem Training deep denoisers with high-quality ground truth images is often impractical.
method Uses Stein's unbiased risk estimator (SURE) for training deep neural networks with only noisy images.
result Trained deep denoisers perform similarly to those trained with ground truth images.
Paper optimizes diffusion models for denoising tasks with theoretical guarantees.
problem Lack of theoretical understanding of MSE optimality in diffusion models.
method Inspired by MSE-optimal CME, proposes a novel denoising strategy for diffusion models.
result Demonstrates polynomial-time convergence to the CME under mild conditions.
New algorithm speeds up image denoising to linear time.
problem Bayesian image denoising with Gaussian Markov Random Field.
method Proposes a new algorithm solving in O(n) time.
result Effective in practice with hyperparameter estimation.
New linear denoiser outperforms standard Wiener filter in noisy data.
problem Improving denoising performance for unknown covariance data.
method Synthetically constructed noisy samples to train a linear denoiser using least-squares approximation.
result Optimal denoiser found using the Convex Gaussian Min-Max Theorem (CGMT) for proportional regime.
Heuristic weighting improves denoising score matching without requiring noise distribution assumptions.
problem Improving denoising score matching without assuming noise distribution.
method Demonstrated heteroskedasticity, derived optimal weighting functions, and provided theoretical and empirical comparisons.
result Heuristical weighting function can achieve lower variance than optimal weighting, facilitating more stable and efficient training.
SNORE applies denoiser only on images with noise of adequate level for image restoration.
problem Image restoration challenges with iterative algorithms and denoising.
method SNORE framework using stochastic regularization and stochastic gradient descent.
result SNORE is competitive with state-of-the-art methods on deblurring and inpainting tasks.