Self-supervised fine-tuning corrects SR CNNs for unseen models and artifacts.
problem SR CNNs' lack of robustness to unseen image formation models and generation of artifacts.
method Iterative fine-tuning using a data fidelity loss at test time.
result Successfully corrects SR solutions for unseen models and GAN artifacts.
Deep-learning improves 6x6-mm OCTA angiograms by reducing noise and artifacts.
problem Reduced scan quality in 6x6-mm OCTA angiograms due to undersampling.
method Deep-learning-based high-resolution angiogram reconstruction network (HARNet) trained on 3x3-mm and 6x6-mm angiogram data.
result Reconstructed 6x6-mm angiograms have lower noise and better vascular connectivity.
Combined ML and JVC-SENSE enable high-resolution imaging with fewer shots and less distortion.
problem Severe distortion artifacts and blurring in high-resolution imaging due to shot-to-shot variations in msEPI.
method Employed deep learning to obtain an interim image with minimal artifacts, which was then used in a Joint Virtual Coil Sensitivity Encoding (JVC-SENSE) reconstruction.
result Enabled navigator-free, highly accelerated multishot EPI with fewer shots and improved geometric fidelity.
Paper proposes a new method for better super-resolution images.
problem Improving realism in super-resolution images.
method Extension of Implicit Maximum Likelihood Estimation (IMLE).
result More realistic super-resolved images with reduced artifacts.
Deep learning improves 3D microscopy resolution without matched target images.
problem Anisotropic resolution in volumetric fluorescence microscopy.
method Cycle-consistent generative adversarial network trained on unpaired 2D images.
result Enhanced axial resolution and restored details between imaging planes.
Extended Isolation Forest improves anomaly detection by resolving score artifacts.
problem Anomaly score heat maps suffer from artifacts due to branching operation in binary trees.
method Two approaches proposed: random data transformation and random hyperplane slicing.
result Robustness improved, variance of scores along constant level sets reduced.
StyleNeRF generates high-resolution images with 3D consistency and style control.
problem Generating high-resolution images with fine details and 3D consistency.
method Integrates NeRF into a style-based generator for efficient high-resolution image synthesis.
result Synthesizes high-resolution images at interactive rates with high 3D consistency and style control.
New video super-resolution method robust to multiple degradation models.
problem Current methods are not robust to mismatch between training and testing degradation models.
method Convolutional Neural Network with pseudo-inverse image formation model and perceptual losses.
result Outperforms current state-of-the-art methods and is robust to multiple degradations.
Study benchmarks machine learning for removing EEG artifacts.
problem Removing artifacts from EEGs to improve clinical interpretation.
method Applied various machine learning algorithms to a large artifact recognition dataset.
result Established a benchmark for future research on artifact removal.
Simple method detects deepfakes with few labeled samples.
problem Automated detection of AI-generated fake images.
method Frequency domain analysis followed by basic classifier.
result Achieved 100% accuracy in high-resolution face images with minimal training data.
New method improves image generation for inverse problems using text prompts.
problem Suboptimal performance of existing latent diffusion models for inverse problems.
method Prompt tuning and latent variable projection to optimize text embeddings.
result P2L method outperforms existing methods on various inverse problem tasks.
The paper uses 3D shapes to reveal sundial design adjustments based on latitude.
problem Identifying sundial design adjustments based on installation location.
method Shape analysis in a high-dimensional space, regression in shape space.
result Sundial design adjustments were latitude-dependent.
Single CNN removes multiple ultrasound artifacts.
problem Efficiently remove multiple ultrasound artifacts.
method OT-driven multi-domain unsupervised deep learning.
result Single neural network removes various artifacts.
Study examines how image artifacts impact polyp detection and proposes methods to mitigate their effects.
problem Impact of image artifacts on automated polyp detection accuracy.
method Systematic analysis of six artifact classes, investigation of learning without forgetting framework.
result Artifacts can either benefit or harm polyp detection; learning without forgetting can mitigate some harmful effects.
New method corrects motion artifacts in MR images without paired data.
problem Lack of paired data for supervised training in deep learning for MR motion correction.
method Outlier-rejecting bootstrap subsampling and aggregation, using optimal transport cycleGAN.
result Outperforms existing deep learning methods in correcting motion artifacts from TSM.
A new method selects regions of interest in GC-MS data without prior target selection.
problem Challenges in GC-MS data analysis due to fragmentation and shared fragment ions.
method Uses a pseudo F-ratio moving window (ψFRMV) to automatically select regions of interest. result Algorithm can accurately identify signal regions in GC-MS data.
Pyramid Attention Networks improve image restoration by leveraging self-similarities across scales.
problem Lack of full exploitation of self-similarities in image restoration by recent deep learning methods.
method Introduces a Pyramid Attention module that processes multi-scale feature correspondences to borrow clean signals from coarser levels.
result Pyramid Attention module achieves state-of-the-art results in various image restoration tasks.
Voice conversion (VC) aims at conversion of speaker characteristic without altering content. Due to training data limitations and modeling imperfections, it is difficult to achieve believable speaker mimicry without introducing processing artifacts; performance assessment of VC, therefore, usually involves both speaker…
Deep learning method reduces conebeam artifacts in CT imaging.
problem Conebeam artifacts in CT imaging due to cone angle.
method Differentiated backprojection domain deep learning for data-driven inversion.
result Our method outperforms existing iterative methods with reduced runtime complexity.
Normalizing flows improve ptychography reconstruction quality and uncertainty quantification.
problem Challenges in ptychography due to large-scale nonlinear and non-convex inverse problems and photon statistics.
method Use of normalizing flows to model the posterior distribution and quantify reconstruction uncertainty.
result Normalizing flows enable better characterization and uncertainty quantification in ptychography reconstructions.
Proposes a fixed smooth convolutional layer to reduce checkerboard artifacts in CNNs.
problem Checkerboard artifacts in CNNs during upsampling and strided convolution.
method Fixed convolutional layer with adjustable smoothness, applied to four CNNs and GANs.
result Significantly improves classification performance and image generation quality.
Study geodesic X-ray transform and streaking artifacts on simple surfaces or spaces of constant curvature.
problem Streaking artifacts in CT images due to metal regions.
method Geodesic X-ray transform on nontrapping compact Riemannian manifolds with strictly convex boundaries.
result Streaking artifacts result from conormal singularities along common tangent geodesics.
Paper introduces adversarial lossy compression for video artifacts reduction.
problem Unpleasant reconstruction artifacts in standard video coding schemes at low bit-rates.
method Adversarial lossy video compression model minimizing an adversarial distortion objective.
result Reduction of perceptual artifacts and detail reconstruction under extreme compression.
Partial-input models fail to detect dataset artifacts, even when they perform poorly.
problem The effectiveness of partial-input models in detecting dataset artifacts is questionable.
method Design artificial datasets and identify trivial patterns in the SNLI dataset.
result Partial-input models can solve examples previously considered hard, indicating potential dataset artifacts.
A new unsupervised method removes CT metal artifacts using beta-CycleGAN and attention.
problem Metal artifact reduction in computed tomography (CT) images.
method Unsupervised learning using a beta-CycleGAN architecture with attention mechanism.
result Improved metal artifact removal that preserves image details.
Single model corrects JPEG artifacts for various compression settings.
problem JPEG compression artifacts due to aggressive quantization.
method Parameterized architecture using quantization matrix.
result State-of-the-art performance across different quality settings.
Accelerated magnetic resonance (MR) scan acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts …
Study identifies numerical signs of blow-up in hydrodynamic equations.
problem Determining if numerical results of blow-up are genuine or artifacts.
method Geometrically consistent spatiotemporal discretization of complexified Euler equations.
result Identification of a signature based on supremum norm growth rates of vorticity.
Deep learning improves MRI image quality from down-sampled data.
problem Improving MRI image quality from accelerated, down-sampled k-space data.
method Deep Residual Dense U-Net architecture with Residual Dense Block and new loss function.
result The proposed method achieves better performance in reconstructing high-quality images from down-sampled k-space data.
Paper explores unsupervised learning for ultrasound image artifact removal.
problem Improving visual quality of ultrasound images from various artifacts.
method Inspired by optimal transport cycleGAN, unsupervised deep learning for artifact removal.
result Unsupervised learning method provides comparable results to supervised learning.
X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using la…
New method for fMRI missing value imputation improves robustness.
problem High frequency of missing values in fMRI data.
method Spatial and time-dependent regularization with a novel recurrent layer.
result Improved robustness against state-of-the-art alternatives.
Interpretation of electroencephalogram (EEG) signals can be complicated by obfuscating artifacts. Artifact detection plays an important role in the observation and analysis of EEG signals. Spatial information contained in the placement of the electrodes can be exploited to accurately detect artifacts. However, when few…
Paper compresses neural network weight-updates for image artifacts removal.
problem Efficiently compressing neural network weight-updates for image artifacts removal.
method Fine-tuning a pre-trained artifact removal network on target data with a compression objective that encourages sparse and quantized weight-updates.
result Achieves reconstruction quality comparable to traditional codecs at comparable bitrates.
L-CNNs approximate gauge actions, revealing fixed points with no lattice artifacts.
problem Approximating gauge actions with lattice artifacts.
method Lattice gauge-equivariant convolutional neural networks (L-CNNs).
result L-CNNs provide fixed point actions with no lattice artifacts.
Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation. In this paper, we propose a set of improvements, which together result in both quantitatively and …
Generative model predicts menstrual cycle lengths accounting for self-tracking artifacts.
problem Uncertainty in self-tracked health data due to user adherence.
method Hierarchical, generative model using machine learning.
result Model yields state-of-the-art performance in predicting menstrual cycle lengths.
Estimates support in distributions with sampling artifacts and errors.
problem Support estimation in the presence of sampling artifacts and errors.
method Regularized weighted Chebyshev approximations with Touchard polynomials, discretized semi-infinte programming.
result Significant improvements over noiseless support estimation methods.
Improved StyleGAN model enhances image quality and invertibility.
problem Characteristics artifacts in StyleGAN's image quality.
method Redesigned generator normalization, revisited progressive growing, and introduced a capacity problem.
result Improved image quality and easier inversion of generated images.
NTK reveals order and chaos in DNNs, affecting checkerboard and border artifacts.
problem Checkerboard and border artifacts in DNNs.
method Analysis using Neural Tangent Kernel (NTK) in infinite-width setting.
result Transition between order and chaos regimes affects DNN performance.
3D dust map of the Milky Way improves resolution and accuracy.
problem Reconstructing the 3D dust distribution in the Milky Way.
method Gaussian process regression on spherical coordinates with iterative grid refinement.
result Improved 3D dust map with increased resolution and accuracy.
New neural network reduces CT radiation, works for any ROI size.
problem CT ROI reconstruction suffers from cupping artifacts and high computation.
method Two neural networks: one learns ROI-specific artifacts, the other learns DBP inversion.
result New network outperforms existing methods for any ROI size.
New method improves autofocus in CBCT scans by 93%.
problem Improper geometry information leads to misplaced signals in CBCT.
method Learning-based motion estimation combined with CBCT consistency constraint.
result Average artifact suppression of 93% achieved.
This article deals with stability issues related to geodesic X-ray transforms, where an interplay between the (attenuation type) weight in the transform and the underlying geometry strongly impact whether the problem is stable or unstable. In the unstable case, we also explain what types of artifacts are expected in te…
Develops a multichannel deep network for faster, artifact-free image CS.
problem Block-wise sampling artifacts in image CS with multiple sampling rates.
method Multichannel deep network for block-based image CS, removing blocking artifacts.
result Significantly outperforms state-of-the-art CS methods in objective and subjective metrics.
In this article, we analyze the microlocal properties of the linearized forward scattering operator F and the normal operator F∗F (where F∗ is the L2 adjoint of F) which arises in Synthetic Aperture Radar imaging for the common midpoint acquisition geometry. When F∗ is applied to the scattered d…
We consider linear models where d potential causes X1,...,Xd are correlated with one target quantity Y and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression c…
We study the integral transform over a general family of broken rays in R2. It is natural for broken rays to have conjugate points, for example, when they are reflected from a curved boundary. If there are conjugate points, we show that the singularities cannot be recovered from local data and therefore art…