Snake solves large graph optimization problems with fast proximal steps.
problem Optimization over large unstructured graphs with graph-specific regularization.
method Snake algorithm using random simple paths for proximal gradient steps.
result Convergence proven for the Snake algorithm.
Probabilistic inpainting learns multiple plausible images from missing data.
problem Generating multiple plausible images from missing data in images.
method Building a PixelCNN model that learns a distribution of images conditioned on visible pixels.
result The method produces diverse and realistic inpaintings.
Theoretical justification for image inpainting using diffusion models.
problem Improving sample recovery in image inpainting without retraining.
method Analysis of RePaint algorithm and proposing RePaint+ to correct misalignment. result RePaint+ algorithm provably recovers the true sample with linear convergence. Physics-informed semantic inpainting improves geostatistical modeling by incorporating indirect measurements.
problem Inferring heterogeneous geological fields from limited measurements and prior spatial statistics.
method Proposes a physics-informed semantic inpainting framework using WGAN-GP to incorporate both direct and indirect measurements.
result The method satisfies physical conservation laws and enhances inpainting performance compared to using only direct measurements.
New method separates objects from images using deep neural networks trained to inpaint.
problem Fully self-supervised instance separation of occluded objects in images.
method Maximizes independence of two image regions given a fully self-supervised inpainting network.
result Method achieves similar segmentation performance to fully supervised methods on microscopy image datasets.
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…
Deep learning models improve chest x-ray inpainting.
problem Improving chest x-ray inpainting using deep generative models.
method Training three deep learning models (context encoders, semantic image inpainting, contextual attention) on 1.2M patches from 60K healthy x-rays.
result Deep learning models can generate highly realistic chest x-ray images.
New method fills MS lesions more realistically and naturally.
problem Lesions in MS brain scans bias morphometric analyses.
method Non-local partial convolutions (NLPC) integrating Unet-like network and non-local module.
result NLPC generates inpainted regions that appear more realistic and natural.
GACELA fills long gaps in musical audio with a GAN and context conditioning.
problem Restoring long gaps in musical audio with varying complexity and duration.
method Generative adversarial network (GAN) with five parallel discriminators and context conditioning.
result Reduced artifacts in inpaintings from unacceptable to mildly disturbing.
EnSF uses image inpainting to handle partial observations in data assimilation.
problem Data assimilation challenges with partial observations.
method EnSF integrates image inpainting with diffusion models to predict unobserved states.
result EnSF successfully tracks SQG dynamics with partial observations.
Unsupervised image inpainting models generate plausible reconstructions from incomplete data.
problem Inpainting without paired or unpaired training data.
method Conditional GAN with latent component dependency for generating image distributions.
result Model generates a distribution of plausible images from incomplete observations.
We address the problem of surface inpainting, which aims to fill in holes or missing regions on a Riemann surface based on its surface geometry. In practical situation, surfaces obtained from range scanners often have holes where the 3D models are incomplete. In order to analyze the 3D shapes effectively, restoring the…
Generative adversarial network improves audio inpainting for long gaps.
problem Generating missing audio content in long-range gaps using WGAN.
method Proposed WGAN architecture with short-range and long-range neighboring borders.
result The proposed model outperforms classical WGAN in reconstructing high-frequency content.
A method for user-controlled semantic image filling.
problem Generating coherent images with user-specified semantics.
method Deep generative model combining encoder, latent variables, and PixelCNN.
result User can control the inpainting of unobserved pixels while maintaining semantic coherence.
Extended RDS filtering for positions and orientations, improving crossing structure enhancement and inpainting.
problem Enhancing and inpainting images with crossing structures.
method Extended RDS filtering to M2 space, using gauge frames to mitigate issues. result RDS filtering outperforms existing techniques in denoising and inpainting images with crossing structures.
Model generates music to connect missing parts, leveraging latent space of VAE.
problem Music inpainting from missing or lost information.
method Deep learning model using VAE latent space and RNN to traverse latent space conditioned on past and future musical contexts.
result Model generates meaningful music inpaintings, connecting musical excerpts.
Develops a test for strict stationarity in stochastic processes.
problem Testing strict stationarity of discrete time stochastic processes.
method Window averaged sample estimate of second order cumulant spectrum, asymptotic complex standard normal distribution test.
result Test statistic derived and demonstrated with 137Cs gamma ray decay data.
New method denoises and fills in missing image data without clean training data.
problem Denoising and inpainting images with noisy, incomplete data.
method Robust Hadamard Autoencoders trained on noisy, incomplete data.
result Autoencoders can perform denoising and inpainting simultaneously.
End-to-end framework for static image generation from dynamic content.
problem Generating static images from dynamic content with occluded backgrounds.
method Conditional GAN for static image generation, convolutional network for dynamic object detection.
result Generated static images are realistic and can be used for augmented reality and robot localization.
Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, beyond standard factorial "sparse" methodology. We derive a large …
Faster matrix completion through randomized SVD algorithms.
problem Efficiently completing large sparse matrices for applications like image inpainting and recommender systems.
method Proposed two fast randomized algorithms (rSVD-PI and rSVD-BKI) and a new subspace recycling technique to accelerate singular value thresholding (SVT) method.
result The proposed algorithms achieve up to 15X faster computation time for image inpainting and movie rating estimation problems.
This work relaxes GNN symmetries to approximate automorphisms, improving model performance.
problem Improving graph neural network performance on asymmetric graphs.
method Formalizing approximate symmetries via graph coarsening, introducing a bias-variance formula.
result Best generalization performance achieved by choosing a larger symmetry group than automorphisms but smaller than permutations.
Paper proposes a new method for recovering missing samples in images.
problem Missing sample recovery in image signals.
method Iterative sparse recovery algorithm using constrained l1-norm minimization with a new CSIM fidelity metric. result Simulation results demonstrate the efficiency of the proposed method.
A new tensor-factor analysis model improves image denoising and classification.
problem Improving image denoising and classification performance.
method Introduces a deep convolutional tensor-factor analysis model for multi-way data.
result Improves PSNR by over 1dB in multi-way denoising and image classification.
CosmoVAE uses deep learning to fill in missing parts of the cosmic microwave background map.
problem Missing observations in the CMB map due to thermal dust noise.
method Variational autoencoder (VAE) trained with a new loss function.
result CosmoVAE achieves state-of-the-art performance in CMB map inpainting.
KeyIn discovers keyframes to simplify video analysis.
problem Extracting essential dynamics from videos interleaved with predictable details.
method A hierarchical Keyframe-Inpainter (KeyIn) model learns keyframes and fills in the rest.
result KeyIn finds informative keyframes across various video dynamics.
A neural model samples arbitrary features conditioned on some inputs.
problem Sampling arbitrary features conditioned on some inputs.
method Variational autoencoder with stochastic variational Bayes training.
result Effectiveness and diversity of generated samples on synthetic data and feature problems.
New method solves linear inverse problems using diffusion models.
problem Linear inverse problems in various domains.
method Posterior sampling with latent diffusion models.
result Provable sample recovery in linear models, outperforming previous methods.
Paper improves dictionary learning from incomplete data.
problem Learning dictionaries from incomplete or corrupted data.
method Adapts ITKrM algorithm to incomplete/masked data, incorporating low-rank components.
result ITKrMM outperforms existing methods in terms of speed and reconstruction quality.
Estimates localized complexity of white-matter wiring using GANs.
problem Complexity of white-matter wiring, especially in ambiguous regions, confounds analysis.
method Bayesian estimate of heteroscedastic aleatoric uncertainty through image inpainting.
result Localized wiring complexity quantified, directly reflecting difficulty of lesion inpainting.
Physics-informed neural networks improve baryonic predictions from dark matter simulations.
problem Recreating hydrodynamic simulations from dark matter requires expensive and time-consuming computations.
method Combining neural network architectures with physical constraints and using Kullback-Leibler divergence for prediction comparison.
result Improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the metallicity relation, and preserved scatter.
The paper proposes a method to learn discriminative multilevel dictionaries for supervised image classification.
problem Improving sparse representation for supervised image classification.
method Learning structured multilevel dictionaries with discriminative constraints for each class, using reconstruction errors of image patches.
result Competitive results compared to state-of-the-art methods on texture image classification.
Generative models improve image classifier explanations by realistically removing features.
problem Perturbation-based explanations often produce unrealistic counterfactual samples.
method Integrate generative inpainters into attribution methods to remove input features.
result Improved explanation methods in object localization, deletion, and saliency metrics.
PG-IM uses neural-symbolic programs to manipulate images.
problem Creating holistic image representations and manipulations.
method PG-IM detects patterns, induces symbolic programs, and manipulates images using a neural network.
result PG-IM learns from a single image and achieves superior performance.
The paper uses machine learning to predict missing yield parameters from liquid markets to illiquid corporate bonds.
problem Predicting missing yield parameters from illiquid corporate bonds.
method Applying Denoising Autoencoder (DAE) algorithm to historical data of liquid market instruments.
result DAE algorithm outperforms point-in-time inpainting algorithms in predicting unobserved yield surfaces.
PAC-Bayesian matrix completion with a spectral scaled Student prior offers efficient inference.
problem Matrix completion with underlying low-rank structure.
method Spectral scaled Student prior and PAC-Bayesian bounds.
result Minimax-optimal oracle inequality for model misspecification and general sampling distribution.
Generative Adversarial Networks create realistic geology from sparse measurements.
problem Building models of subsurface geology from sparse physical measurements.
method Semantic inpainting with Generative Adversarial Networks.
result Generated samples mimic a distribution of geological patterns, not a single image.
Ada-LISTA adapts neural solvers for varying models.
problem Adapting neural solvers for varying models.
method Ada-LISTA receives pairs of signals and dictionaries, learns a universal architecture, and solves sparse coding in linear rate.
result Ada-LISTA solves sparse coding in linear rate for varying models.
Extends RDS filtering to position-orientation space for better image processing.
problem Enhancing and inpainting images with crossing structures.
method Created a version of RDS filtering using gauge frames, studying generalised diffusion.
result RDS filtering on position-orientation space improves denoising and inpainting of crossing structures.
RGI improves robustness of GAN-inversion for image restoration and anomaly detection.
problem Robustness of GAN-inversion to unknown gross corruptions.
method Proposes RGI and R-RGI methods with provable robustness guarantees.
result Restored images and corrupted region masks converge to ground truth under mild assumptions.
Collaborative filtering system restores images with impulse noise.
problem Restoring images with identified noisy pixels.
method Defines a new color image representation using matrices and parameters, performs experiments on a database.
result Algorithm performance depends on k and λ parameters, providing guidance on their selection. Hinge-FM2I fills missing data in time series with high accuracy.
problem Handling missing data in univariate time series.
method Inspired by door hinges, Hinge-FM2I imputes missing data using FM2I and selects the best imputed gap.
result Hinge-FM2I significantly outperforms other methods in sMAPE scores.
The problem of recovering a low n-rank tensor is an extension of sparse recovery problem from the low dimensional space (matrix space) to the high dimensional space (tensor space) and has many applications in computer vision and graphics such as image inpainting and video inpainting. In this paper, we consider a new …
Combines variational and evolutionary optimization for generative models.
problem Optimizing generative models with discrete latent variables.
method Truncated posteriors as variational distributions, evolutionary algorithms applied to variational parameters.
result Evolutionary algorithms effectively optimize variational bounds for generative models.
New method improves diversity in GAN-generated images.
problem Lack of diversity in GAN-generated images.
method Introducing moment reconstruction losses to replace the reconstruction loss in GAN training.
result Improved diversity in generated images without sacrificing visual fidelity.
NeuroPaint infers missing brain area dynamics from multi-animal datasets.
problem Leveraging multi-animal datasets to understand interactions between brain areas.
method Masked autoencoding approach trained across animals with partial observations.
result Models can successfully reconstruct dynamics of unrecorded brain areas.
Deep learning model fills missing data in digital elevation models.
problem Generating missing data in digital elevation models.
method Wasserstein Generative Adversarial Network (GAN) with contextual attention mechanism.
result The model successfully fills voids in digital elevation models.
OnAIR reconstructs dynamic images from sparse measurements online.
problem Reconstructing dynamic images from limited or corrupted measurements.
method Online adaptive reconstruction using sparsity and low-rank models with dictionary learning.
result Memory-efficient online algorithms for sequential estimation of dictionary and images.