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 3551–3575 of 3874 papers

TitleStatusHype
Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes—0
Feature Super-Resolution: Make Machine See More Clearly—0
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From ShadingCode0
“Zero-Shot” Super-Resolution Using Deep Internal Learning—0
Face Recognition in Low Quality Images: A Survey—0
On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques—0
Face hallucination using cascaded super-resolution and identity priors—0
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution—0
A hybrid approach of interpolations and CNN to obtain super-resolution—0
Structured Bayesian Gaussian process latent variable model—0
PiPs: a Kernel-based Optimization Scheme for Analyzing Non-Stationary 1D Signals—0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Learning Dual Convolutional Neural Networks for Low-Level Vision—0
Enhanced Signal Recovery via Sparsity Inducing Image Priors—0
The Domain Transform Solver—0
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-ResolutionCode0
Superresolution method for data deconvolution by superposition of point sources—0
Image Super-Resolution via Dual-State Recurrent NetworksCode0
Large Receptive Field Networks for High-Scale Image Super-Resolution—0
Super-resolution Ultrasound Localization Microscopy through Deep Learning—0
Densely Connected High Order Residual Network for Single Frame Image Super Resolution—0
Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach—0
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution—0
A two-stage 3D Unet framework for multi-class segmentation on full resolution image—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified