Paper proposes SMFN for high-res spherical video super-resolution.
problem Super-resolution of 360-degree panoramic videos is expensive and challenging.
method Deformable convolutions, mixed attention mechanism, dual learning strategy, weighted mean square error loss function.
result The proposed SMFN method improves super-resolution of equatorial regions in 360-degree videos.
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.
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images. Current state-of-the-art methods process a batch of LR frames to generate a single high-resolu…
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly…
New method improves local precipitation predictions using video diffusion.
problem Limited high-resolution local precipitation predictions due to computational costs.
method Extends video diffusion models to capture conditional distribution of high-resolution patterns.
result Method outperforms state-of-the-art baselines in CRPS, MSE, and precipitation distribution.
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.
VISION-XL improves HD video quality using latent image diffusion models.
problem Improving high-definition video quality and resolution.
method Latent image diffusion models and pseudo-batch consistent sampling.
result State-of-the-art video reconstruction across various inverse problems.
New method tackles video inverse problems using image diffusion models.
problem Spatio-temporal degradation in video inverse problems.
method Leverages image diffusion models to treat time dimension as batch dimension, introduces batch-consistent diffusion sampling.
result Achieves state-of-the-art reconstructions for various spatio-temporal degradations.
We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-exploiting algebra on the model covariance matrices for computational tractability. Inference is made tractable through a collapsed variation…
Study shows zero-shot super-resolution in neural operators is impossible in many cases.
problem Understanding the theoretical limits of zero-shot super-resolution in neural operators.
method Systematic theoretical study including information-theoretic and generalization bounds analysis.
result Zero-shot super-resolution is information-theoretically impossible in many settings.
Neural DEs improve single image super-resolution.
problem Challenging tasks in image super-resolution.
method Applied Neural Differential Equations to image super-resolution, using variational methods and backpropagation.
result Differential models match state-of-the-art performance.
Bayesian framework for solar magnetogram super-resolution with uncertainty quantification.
problem Uncertainty in super-resolving solar magnetic field images.
method Bayesian decomposition of uncertainties into epistemic and aleatoric.
result Generation of maps measuring the range of possible high-resolution explanations.
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
problem Domain shift in low-resolution 4D Flow MRI data.
method Distributional deep learning framework for domain generalization.
result Framework significantly outperforms traditional methods in real data applications.
A new method uses conformal prediction to create reliable confidence masks for image super-resolution.
problem Uncertainty quantification in image super-resolution using generative models.
method Conformal prediction techniques applied to a confidence mask for reliable uncertainty communication.
result Strong theoretical guarantees and empirical solid performance in image super-resolution.
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.
New framework compresses and recovers scientific data efficiently.
problem Efficiently managing and recovering from large scientific datasets.
method Grounded in learning exponential families, preserves uncertainty and supports trade-offs.
result Preserves physical features and quantities of interest in compressed representations.
Generative adversarial networks generate realistic, time-evolving high-resolution atmospheric fields.
problem Improving spatial resolution of low-resolution atmospheric images.
method Recurrent, stochastic super-resolution GAN for generating ensembles of time-evolving high-resolution atmospheric fields.
result The GAN produces realistic, temporally consistent super-resolution sequences for radar-measured precipitation and cloud optical thickness.
Paper tackles super-resolution of measures with machine learning techniques.
problem Approximating a finitely supported measure from its coefficients.
method Defining a distance metric, explicit expression for recuperation operator, and estimating approximation quality.
result Best possible estimates for approximation quality are shown.
Deep learning speeds up whole heart MRI to 30 seconds.
problem Long acquisition times in whole heart MRI.
method Deep learning, specifically a 3D residual U-Net, to reconstruct high-resolution images from low-resolution data.
result Super-resolution images show better edge sharpness and fewer artefacts than low-resolution images.
Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal conv…
MeshfreeFlowNet generates high-resolution spatio-temporal solutions from low-resolution inputs.
problem Generating high-resolution spatio-temporal solutions from low-resolution inputs.
method Physics-constrained deep learning framework using fully convolutional encoders.
result Significantly outperforms existing baselines in super-resolution of turbulent flows.
A cost-effective method to generate high-resolution images using wavelet-based super-resolution.
problem High compute resources required for training state-of-the-art image generation models.
method Wavelet-based two-step training framework: low-frequency sampling followed by super-resolution.
result Achieves FID of 10.59 on ImageNet 512x512 with half the compute of BigGAN.
GANs can learn hierarchical distributions in real-world images efficiently.
problem Understanding and efficiently learning complex, real-world distributions with GANs.
method Formally studying how GANs can learn hierarchically generated distributions close to real-life image distributions using SGDA.
result Training GANs via SGDA can efficiently learn distributions with a 'forward super-resolution' structure, both in sample and time complexities.
Improved satellite object detection with NN upscaling.
problem Improving object detection accuracy on small objects in satellite images.
method Comparison of Super-Resolution (SR) and Nearest Neighbors (NN) interpolation on xView satellite data.
result NN upscaling yields nearly identical object detection results to SR with a 0.0002 AP difference.
SRFRN accelerates image super-resolution using shallow residual units.
problem High computational complexity and time in deep learning image super-resolution.
method SRFRN uses a bicubic interpolated low-resolution image and residual representative units (RFR) for faster and more efficient high-resolution image reconstruction.
result SRFRN achieves superior performance and faster execution time compared to existing methods.
This paper improves image super-resolution by integrating cross-scale non-local attention.
problem Improving image super-resolution by leveraging long-range and cross-scale feature correlations.
method Proposes a Cross-Scale Non-Local (CS-NL) attention module integrated into a recurrent neural network.
result Significantly improved performance on SISR benchmarks.
Enhances VAEs for sharper image synthesis.
problem Blurriness in generated images from VAEs.
method Integrates a downscaled version of the original image into the VAE framework and uses it as input to the decoder.
result Improves FID score in image synthesis while maintaining similar log-likelihood performance.
Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a nonhomogeneous Poisson process. The estimator is the super-resolution analogue to Shao 2010 and Shao & Lii 2011, which is a sum of p sinusoids where p and the frequency, amplitud…
Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns, called dictionary elements, from the data. Because it is nonparametric…
New neural network captures spatial correlations in wind speed predictions.
problem Uncertainty quantification in neural network predictions for high-dimensional, correlated data.
method Training neural networks with multidimensional Gaussian loss, preserving spatial correlation and computational tractability.
result Demonstrated super-resolution of surface wind speed with explicit correlation modeling.
Deep learning solves wave-based inverse problems, including super-resolution imaging.
problem Solving inverse wave scattering problems across all length scales.
method Wide-band butterfly network coupled with dynamic noise injection.
result Framework successfully solves super-resolution imaging problems.
With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often di…
Probabilistic SR method speeds up high-fidelity simulations with reliable uncertainty estimates.
problem Lack of reliable uncertainty quantification in deep-learning based SR methods.
method Statistical Finite Element Method and energy-based generative modeling.
result Efficient high-resolution predictions with inherent uncertainty estimates.
Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an altern…
DeepAM optimizes deep neural networks for image super-resolution.
problem Image super-resolution using deep neural networks.
method L-layer analysis dictionary model with IPAD and CAD.
result DeepAM outperforms deep neural networks with back-propagation.
HighRes-net enhances satellite imagery by fusing multiple low-res views.
problem Super-resolving satellite imagery for reliable monitoring of human impact.
method End-to-end deep learning approach for multi-frame super-resolution.
result HighRes-net outperformed in European Space Agency's MFSR competition.
With the fast growth in the visual surveillance and security sectors, thermal infrared images have become increasingly necessary ina large variety of industrial applications. This is true even though IR sensors are still more expensive than their RGB counterpart having the same resolution. In this paper, we propose a d…
Time-aware deep learning methods improve spatial downscaling of atmospheric pollutants.
problem Transform coarse satellite data of atmospheric pollutants into high-resolution fields.
method Super-resolution deep residual networks and UNet architectures are extended with a temporal module encoding observation time.
result Temporal modules significantly improve downscaling performance and convergence speed.
Proposes a cross-scale residual network for multiple image restoration tasks.
problem Image restoration tasks (super-resolution, denoising, deblocking) have strong correlations.
method Cross-scale residual network exploiting scale-related features and inter-task correlations.
result Outperforms state-of-the-art methods in multiple image restoration tasks.
Consistency models generate high-quality samples fast and without iterative sampling.
problem Slow generation in diffusion models.
method Direct mapping of noise to data, supporting fast one-step generation and multistep sampling.
result Consistency models achieve state-of-the-art FID scores and outperform diffusion models in one-step generation.
Over the past decade, many Super Resolution techniques have been developed using deep learning. Among those, generative adversarial networks (GAN) and very deep convolutional networks (VDSR) have shown promising results in terms of HR image quality and computational speed. In this paper, we propose two approaches based…
High resolution magnetic resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware, cost and processing constraints. Recently, deep learning methods have been shown to produce compelling state of the art results for image super-resolution. Paying pa…
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-res…
Ground-A-Video edits videos without training, preserving intended changes.
problem Complex multi-attribute video editing with omitted or wrong changes.
method Grounding-guided video-to-video translation with Cross-Frame Gated Attention.
result Zero-shot multi-attribute video editing with improved accuracy and frame consistency.
Deep learning improves single-molecule localization for super-resolution microscopy.
problem Accurate and efficient localization of single molecules for super-resolution microscopy.
method A novel deep learning network architecture that uses temporal context to simultaneously detect and localize molecules.
result Achieves state-of-the-art performance on the SMLM2016 challenge, excels at high densities.
With the recent advancement in the deep learning technologies such as CNNs and GANs, there is significant improvement in the quality of the images reconstructed by deep learning based super-resolution (SR) techniques. In this work, we propose a robust loss function based on the preservation of edges obtained by the Can…
Novel algorithm accelerates PnP methods for image deblurring and super-resolution.
problem Efficiently solving inverse problems and imaging with provable convergence guarantees.
method Incorporates quasi-Newton steps into provable PnP framework based on proximal denoisers.
result 2--8x faster convergence compared to other provable PnP methods with similar quality.
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.