A k-space deep learning method corrects EPI ghost artifacts without a reference scan.
problem Nyquist ghost artifacts in EPI MRI due to phase mismatch between even and odd echoes.
method Structured low-rank Hankel matrix approaches combined with data-driven Hankel matrix decomposition and deep convolutional neural networks.
result The proposed k-space deep learning method outperforms existing methods in image quality and computing time.
Deep learning speeds MRI by using less data, achieving high-quality images.
problem MRI reconstruction with full k-space data is time-consuming and resource-intensive.
method Sub-Nyquist sampling, image-folding correction, and deep learning.
result Deep learning can reconstruct high-quality MRI images with only 29% of k-space data.
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.
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 uses spoofing countermeasures to assess speech processing artifacts in voice conversion.
problem Difficulty in objectively assessing speech processing artifacts in voice conversion.
method Configured a constant-Q cepstral coefficient (CQC) model to measure artifact extent.
result Identified two clusters of VCC'18 entries: low-quality with detectable artifacts and higher quality with less 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.
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.
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.
Article examines stability of geodesic X-ray transforms and artifacts.
problem Stability of geodesic X-ray transforms and artifacts.
method Analyzes the impact of weight and geometry on stability, and examines artifacts in unstable cases.
result Landweber algorithm cannot provide accurate reconstruction in unstable cases.
Reduced-channel EEG systems struggle with artifact detection, highlighting the importance of referential channels.
problem Artifact detection in EEG signals with fewer channels.
method Investigated a deep learning algorithm, CNN-LSTM, on various channel configurations.
result False alarms increase dramatically when fewer channels are used, emphasizing the importance of referential channels.
Deep learning removes aliasing in MRI scans with fast computation.
problem High computational costs in MR scan reconstruction.
method Deep residual learning networks for magnitude and phase networks.
result Deep learning successfully removes aliasing artifacts from MRI scans.
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.
Artifacts appear in broken ray transform due to conjugate points.
problem Artifacts in broken ray transform due to conjugate points.
method Integral transform over broken rays, analysis of conjugate points.
result Singularities cannot be recovered from local data, leading to artifacts.
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.
Study compares EEG and fMRI systems, finding tradeoffs in artifact removal and classification accuracy.
problem Dealing with artifacts introduced by simultaneous EEG and fMRI recordings.
method Comparison of three MR compatible EEG recording systems, assessing their performance in single-trial EEG classification.
result Tradeoffs across systems, including setup ease and artifact removal methods.
AaSP improves audio self-supervised learning by addressing aliasing issues.
problem Alias issues in audio spectrogram transformers.
method AaSP combines aliasing-aware patch representation, teacher-student masked modeling, cross-attention predictor, and contrastive regularization.
result AaSP learns more stable representations that integrate high-frequency cues.
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.
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.
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.
Detects non-causal artifacts in multivariate regression models.
problem Identifying non-causal associations in multivariate linear regression models.
method Uses ICA-based model to distinguish between causal and artifact associations by analyzing the orientation of regression coefficients relative to the covariance matrix.
result Regression vectors concentrate in low eigenvalue space for confounding and overfitting, distinguishing them from causal relationships.
Abstract: A new approach to technical indicators without lag.
problem Defining classical technical indicators as bounded operators for lag-free trading.
method Using linear algebra to redefine technical indicators as bounded operators in l∞(N) space. result Demonstrated the no-lag versions of technical indicators are simpler and more effective.
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.
Conventional approaches of sampling signals follow the celebrated theorem of Nyquist and Shannon. Compressive sampling, introduced by Donoho, Romberg and Tao, is a new paradigm that goes against the conventional methods in data acquisition and provides a way of recovering signals using fewer samples than the traditiona…
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.
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.
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.
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.
Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains.…
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.
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.
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…
Proposes a machine learning method to evaluate creative artifacts.
problem Need for objective and automatic evaluation of creative artifacts.
method Regression-based learning framework considering novelty, influence, value, and unexpectedness.
result Promising results in predicting movie ratings and identifying creative movies.
Develops a kernel method for computing Wasserstein distance.
problem Lack of kernel methods for nonlinear data in Wasserstein distance.
method Kernel trick to compute L2-Wasserstein distance in a kernel space.
result Kernel approach outperforms classical non-kernel methods in identifying CT slices with artifacts.
Deep learning reduces artifacts in limited angle X-ray microscopy.
problem Artifacts in image reconstruction from limited angle data in TXM.
method Training a U-Net deep neural network from synthetic data to reduce artifacts.
result Significant improvement in image quality and structural similarity.
PPGnet model estimates heart rate from PPG signals without motion artifacts.
problem Wearable PPG devices struggle with motion artifacts.
method End-to-end deep learning model using 8-second PPG signals.
result Achieved mean absolute error of 3.36+-4.1 BPM on IEEE SPC 2015 dataset.
We present techniques for effective Gaussian process (GP) modelling of multiple short time series. These problems are common when applying GP models independently to each gene in a gene expression time series data set. Such sets typically contain very few time points. Naive application of common GP modelling techniques…
Enhances MRI image quality with Conditional WGAN and adaptive balancing.
problem Struggles to reconstruct sharp images with fine detail.
method Conditional Wasserstein Generative Adversarial Network (WGAN) with Adaptive Gradient Balancing.
result Produces sharper images than other techniques.
Study learns mixtures of smooth product distributions from samples.
problem Learning mixtures of non-parametric product distributions.
method Two-stage approach using identifiability properties of tensor decomposition and signal processing techniques.
result Recovery of component distributions under a smoothness condition.
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods have shown great potential in removing the complex and spatial-variant noises in CT images. However, some residue artifacts would appear in…
Neural time-series data contain a wide variety of prototypical signal waveforms (atoms) that are of significant importance in clinical and cognitive research. One of the goals for analyzing such data is hence to extract such 'shift-invariant' atoms. Even though some success has been reported with existing algorithms, t…
Analyzes GAN units for better understanding and improvement.
problem Lack of understanding GAN internal representations and artifacts.
method Interpretable units identification, causal effect quantification, contextual relationship examination.
result Visualizes and understands GANs at various levels, enabling new insights and improvements.
Unified AI detection framework for various artifacts.
problem Effective oversight and regulation of AI deployment.
method Unified detection framework based on Mahalanobis distance scores (MDS).
result Efficient and robust estimation of covariance matrix for positive samples.