PixelDCL addresses checkerboard problem in deconvolutional layers.
problem Checkerboard problem in deconvolutional layers.
method Fresh interpretation of deconvolution operation to establish direct relationships among adjacent pixels.
result PixelDCL can consider spatial features and yields more accurate segmentation outputs.
New CNN method improves deconvolution microscopy without PSF measurement.
problem Computational expense and blind estimation in conventional deconvolution microscopy.
method Cycle consistent CNN with explicit PSF modeling layers for blind deconvolution.
result Algorithm robustness and efficacy confirmed through experimental results.
A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up and top-down probabilistic learning. After learning the deep convolutional dictionary, testing is implemented via dec…
Network deconvolution removes redundant data to improve neural network performance.
problem Redundant data in neural networks makes training challenging.
method Network deconvolution optimally removes pixel-wise and channel-wise correlations before each layer.
result Network deconvolution improves performance across various datasets.
End-to-end image super-resolution using Attention-based DenseNet with residual deconvolution.
problem Challenging task of improving low-resolution images.
method Proposes a novel ADRD model with weighted dense blocks and spatial attention modules.
result Demonstrates promising performance on publicly available datasets.
Bayesian framework integrates spectral deconvolution with expert reasoning for robust peak estimation.
problem Challenges in extracting meaningful peaks from noisy or complex spectra.
method Bayesian spectral deconvolution coupled with a physical-property regression layer.
result Recovery of weak peaks in poly(lactic acid) IR spectra related to degradation rates.
Deep model generates and analyzes images using hierarchical convolutional learning.
problem Representation and analysis of images.
method Hierarchical convolutional dictionary-learning framework with stochastic unpooling, Bayesian support vector machine, and deep deconvolutional inference.
result Excellent results on benchmark datasets, competitive with convolutional neural networks.
DDN models flexible free-form conditional distributions.
problem Difficulty in explicitly approximating arbitrary conditional distributions.
method Deconvolutional neural network framework for discretizing continuous domains.
result DDN outperforms other density-estimation methods on various tasks.
A novel Gaussian process approach for deconvolution of missing data signals.
problem Recovering a latent source from observations with missing parts and unknown filters.
method Gaussian process prior for Bayesian nonparametric deconvolution.
result The proposed Gaussian process deconvolution (GPDC) method is feasible and effective.
We present a novel architecture, the "stacked what-where auto-encoders" (SWWAE), which integrates discriminative and generative pathways and provides a unified approach to supervised, semi-supervised and unsupervised learning without relying on sampling during training. An instantiation of SWWAE uses a convolutional ne…
New algorithm robustly solves blind deconvolution problems.
problem Robustly solving blind deconvolution problems in the presence of noise and perturbations.
method Mirror Descent algorithm for robust continuous optimization.
result Provable robustness and convergence guarantees for the algorithm.
A fast framework for image deconvolution with incomplete observations.
problem Deconvolution with unknown boundaries is slow and artifacts-prone.
method Diagonalization of convolution operators, iterative pixel estimation and deconvolution.
result Framework allows fast deconvolution with unknown boundaries.
Deep learning improves galaxy image deconvolution in surveys.
problem Deconvolving large survey images with space-variant PSFs.
method Employed a U-Net DNN architecture for supervised galaxy image processing. Two strategies: Tikhonov deconvolution and ADMM-based iterative deconvolution.
result Deep learning approaches outperform standard convex optimization techniques in galaxy image reconstruction and shape recovery.
RED-SC improves deep subspace clustering efficiency.
problem Slow convergence in training deep subspace clustering models.
method Residual Encoder-Decoder network with skip-layer connections and self-expressive layer.
result Training converges much faster with RED-SC.
Hybrid method reveals true currency correlations.
problem Identify currency status in foreign exchange networks.
method Combines DCCC and network deconvolution to filter indirect effects.
result Reflects currency status changes and is more stable.
This paper tackles sparse blind deconvolution with short signals and demonstrates recovery of near ground truth kernels.
problem Recovering two unknown signals from their convolution, especially when one is short and sparsely supported.
method Formulated as a nonconvex optimization problem over the sphere, using a descent algorithm that escapes strict saddle points.
result Near shift truncation of the ground truth kernel can be recovered under specific conditions.
Normalizing flows improve density estimation from noisy data.
problem Estimating underlying density from noisy samples.
method Use normalizing flows for density estimation with arbitrary noise distributions, using amortized variational inference.
result Normalizing flows can outperform Gaussian mixtures for density deconvolution.
Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on discretely sampled continuo…
Paper improves calcium signal deconvolution using efficient state-space models.
problem Deconvolving calcium signals from imaging data.
method Dynamic compressed sensing framework with two nested EM algorithms.
result Proves recovery guarantees and derives confidence bounds for state estimates.
Improved text matching model using deconvolutional networks.
problem Text sequence matching challenges.
method Jointly optimizing generative and discriminative objectives with deconvolutional networks.
result Significantly outperforms sentence-encoding baselines, especially in semi-supervised settings.
SegCaps improves object segmentation accuracy with fewer parameters.
problem Object segmentation accuracy and efficiency.
method Convolutional-deconvolutional capsule network with locally-connected routing.
result SegCaps reduces parameter space by 95.4% while improving accuracy.
New methods speed up fitting for large datasets with noisy observations.
problem Fitting large datasets with Gaussian noise and known covariance.
method Two minibatch variants of extreme deconvolution, online EM algorithm, and gradient-based optimisation.
result Methods can scale to larger models and fit larger datasets faster.
Convolutional factor analysis improves compressive sensing with fewer measurements.
problem Efficiently reconstructing images from limited compressed measurements.
method Learn convolutional dictionaries from compressed measurements using ADMM.
result Achieves comparable classification accuracy with 30% fewer measurements.
A new method for accurately reconstructing signals without knowing the kernel or signal regularity.
problem Recovering signals from noisy measurements without prior knowledge of the convolution kernel or signal regularity.
method Parametrizing the convolution kernel and prior length-scales, jointly estimated in the inversion procedure.
result Accurate reconstructions of signals with varying regularity and unknown kernel size.
Detects object edges and assigns class labels without pixel-level annotations.
problem Semantic boundary and edge detection with image-level labels.
method Proposes a novel strategy to perform edge detection and class assignment using whole image neural nets and backpropagation.
result High pixel-wise scores indicate semantic boundary locations, suggesting edge labels are not needed during training.
OmniFold uses deep learning to deconvolve high-dimensional simulations.
problem Removing detector distortions and accounting for noise processes in high-dimensional simulations.
method OmniFold is a deep learning-based approach for maximum likelihood deconvolution.
result OmniFold can remove detector distortions and account for noise processes and acceptance effects.
WaveletGAN improves GANs by homogenizing noise through multi-channel wavelet filtering.
problem Current noise generation models in GANs struggle with homogenizing noise, leading to low-fidelity samples.
method Proposes a multi-channel wavelet-based filtering method to homogenize noise in GANs.
result WaveletGAN generates high-fidelity samples with the smallest FIDs on Fashion-MNIST, KMNIST, and SVHN datasets.
New method solves sparse deconvolution problems with theoretical guarantees and practical applications.
problem Extracting localized, recurring motifs in signals with spatial or temporal structure.
method Geometric approach using sphere constraints and data-driven initialization to derive a provable algorithm.
result Practical algorithm solves real-world deconvolution problems with good performance and generalizability.
This paper develops a new method for online density estimation from noisy data.
problem Estimating probability density function from noisy streaming data.
method Quasi-Bayesian sequential deconvolution using Newton's algorithm.
result Sequential deconvolution estimate fn with large sample asymptotic guarantees. New method separates and deconvolves signals from single-channel mixtures.
problem Separating and deconvolving individual sources from a single-channel mixture.
method Synthesizing-decomposition (S-D) approach using GAN for sources and optimization for filters and sources.
result Achieves PSNR improvements over existing methods in various tasks.
New algorithm profiles nuisance parameters for multidimensional deconvolution.
problem Statistical removal of instrumental effects in experimental measurements.
method Iterative algorithm called Profile OmniFold that profiles nuisance parameters.
result Illustrated with a Gaussian example, highlighting promising capabilities.
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF…
GANs improve galaxy image recovery beyond deconvolution limits.
problem Limited recovery of galaxy features from noisy, low-resolution images.
method Training a GAN on galaxy images to recover features from degraded data.
result GANs can recover features from degraded images better than simple deconvolution.
Nonparametric density deconvolution and denoising using simulation-based inference
problem Learning latent signals and their distributions in the presence of measurement noise
method Convolutional maximum mean discrepancy (convMMD) loss and likelihood-free framework
result Learn a latent generative model matching observed data distribution
New algorithm for tensor decomposition and Gaussian mixture models.
problem Efficiently decompose overcomplete order-3 tensors and estimate parameters of Gaussian mixtures.
method Proposes Jennrich's algorithm adapted for tensor decomposition and Gaussian mixture models.
result Efficient algorithm for decomposing symmetric overcomplete order-3 tensors and estimating parameters of Gaussian mixtures.
Study characterizes spike deconvolution basin for noisy data.
problem Recover spike locations from noisy convolution with PSF across multiple snapshots.
method Variable-projection formulation, explicit basin of convexity characterization, local convergence guarantees.
result Consistent estimator within basin of convexity under stochastic noise, complementary error bound under adversarial noise.
Local minimax analysis for Poisson deconvolution of discrete signals.
problem Estimating a discrete uniform signal from Poisson convolutions.
method Local minimax risk analysis of a broad class of kernels.
result Sharp estimation rates as a function of signal clustering.
New Bayesian method for spectral deconvolution with Poisson noise.
problem Estimating physical model parameters from noisy spectral data.
method Bayesian measurement framework applied to Poisson noise model.
result Clarifies relationship between measurement time and estimation limits.
Two-stage nonconvex algorithm and convex relaxation both achieve optimal accuracy in noisy blind deconvolution.
problem Solving bilinear systems of equations with random noise under different designs.
method Two-stage nonconvex algorithm and convex relaxation.
result Both methods achieve minimax-optimal accuracy in the presence of random noise.
Gradient descent converges globally for over-parameterized models using optimal transport.
problem Training neural networks with a large hidden layer.
method Gradient descent on particle weights and positions in the Wasserstein space.
result Gradient flow converges to global minimizers in the many-particle limit.
A new method for joint noise removal and trend estimation from sparse signals.
problem Jointly removing noise and estimating trends from sparse signals.
method PENDANTSS combines SOOT/SPOQ penalties with BEADS algorithm in a Trust-Region block alternating variable metric forward-backward approach.
result Outperforms comparable methods in deconvolving analytical chemistry signals.
Convolutional autoencoding improves long text reconstruction.
problem Text reconstruction quality decreases with text length.
method Sequence-to-sequence, purely convolutional and deconvolutional autoencoding.
result Better at reconstructing and correcting long paragraphs.
Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling. Experimental solutions to mitigate tissue heterogeneity are expensive, time consuming, inapplicable to existing data, and may alter the original gene expression patterns. Here we a…
New method uncouples isotonic regression from paired data.
problem Estimating nondecreasing function from unordered paired data.
method Optimal transport theory and moment-matching arguments.
result Efficient algorithm achieves optimal rates under weak moments.
We introduce the blind subspace deconvolution (BSSD) problem, which is the extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. We examine the case of the undercomplete BSSD (uBSSD). Applying temporal concatenation we reduce this problem to ISA. The associated `high …
Paper tackles privacy-preserving data density issues using deconvolution.
problem Privacy-preserving noise affects data density, leading to under/over-estimation.
method Develops deconvoluting kernel density estimators and regression models.
result Demonstrates improved accuracy in estimating heavy-hitters with locally differential data.
Improved graph generation model for small organic molecules.
problem Graph generation models struggle with matching training distributions and require expensive graph matching.
method Introduced a message passing neural network into the GVAE's encoder and decoder.
result Demonstrated improved graph generation for small organic molecules.
An evolutionary algorithm separates mixed DNA profiles in forensic genetics.
problem Deconvolving mixed DNA profiles from crime samples.
method Multiple population evolutionary algorithm (MEA) with guided mutation.
result The MEA successfully deconvoluted DNA profiles from crime samples.