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

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Showing 3526–3550 of 3874 papers

TitleStatusHype
Non-Local Recurrent Network for Image RestorationCode0
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network—0
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images—0
Mesoscopic Facial Geometry Inference Using Deep Neural Networks—0
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior—0
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion CompensationCode0
“Zero-Shot” Super-Resolution Using Deep Internal Learning—0
Feature Super-Resolution: Make Machine See More Clearly—0
Scale-Transferrable Object Detection—0
Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes—0
A Papier-Mâché Approach to Learning 3D Surface Generation—0
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From ShadingCode0
On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques—0
Face Recognition in Low Quality Images: A Survey—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified