SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3801–3850 of 3874 papers

TitleStatusHype
Video Super-Resolution via Deep Draft-Ensemble Learning—0
Rolling Shutter Super-Resolution—0
Conditioned Regression Models for Non-Blind Single Image Super-Resolution—0
Shepard Convolutional Neural NetworksCode0
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
Learning to Generate Images with Perceptual Similarity Metrics—0
Super-Resolution with Deep Convolutional Sufficient StatisticsCode0
Accurate Image Super-Resolution Using Very Deep Convolutional NetworksCode0
Deeply-Recursive Convolutional Network for Image Super-ResolutionCode0
Seven ways to improve example-based single image super resolution—0
Fast Single Image Super-Resolution—0
Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples—0
Super-Resolution Off the Grid—0
Is Image Super-resolution Helpful for Other Vision Tasks?—0
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration—0
Deep Networks for Image Super-Resolution with Sparse Prior—0
Developing a new biophysical tool to combine magneto-optical tweezers with super-resolution fluorescence microscopy—0
Boosting Optical Character Recognition: A Super-Resolution Approach—0
Handling Motion Blur in Multi-Frame Super-Resolution—0
Single Image Super-Resolution From Transformed Self-Exemplars—0
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution—0
Robust Multi-Image Based Blind Face Hallucination—0
Robust Image Filtering Using Joint Static and Dynamic Guidance—0
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling—0
Metric Imitation by Manifold Transfer for Efficient Vision Applications—0
Fast and Accurate Image Upscaling With Super-Resolution Forests—0
Sparse Depth Super Resolution—0
Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning—0
Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images—0
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model—0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction—0
Self-Tuned Deep Super Resolution—0
Novel Super-Resolution Method Based on High Order Nonlocal-Means—0
Single image super-resolution by approximated Heaviside functions—0
Designing A Composite Dictionary Adaptively From Joint Examples—0
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution—0
Parallel Statistical Multi-resolution Estimation—0
Learning Super-Resolution Jointly from External and Internal Examples—0
Super-resolution MRI Using Finite Rate of Innovation Curves—0
Image Super-Resolution Using Deep Convolutional NetworksCode1
First order algorithms in variational image processing—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
Higher-order MRFs based image super resolution: why not MAP?—0
Super-resolution method using sparse regularization for point-spread function recovery—0
Single Image Super Resolution via Manifold Approximation—0
A fast patch-dictionary method for whole image recovery—0
A New Approach for Super resolution by Using Web Images and FFT Based Image Registration—0
Generative Adversarial NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified