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 3351–3375 of 3874 papers

TitleStatusHype
Multigrid Backprojection Super-Resolution and Deep Filter VisualizationCode0
OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality AssessmentCode0
Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New MethodCode0
A Matrix-in-matrix Neural Network for Image Super ResolutionCode0
Multiframe Motion Coupling for Video Super ResolutionCode0
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-ResolutionCode0
MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest DetectionCode0
Volumetric Isosurface Rendering with Deep Learning-Based Super-ResolutionCode0
Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion ModelsCode0
TINQ: Temporal Inconsistency Guided Blind Video Quality AssessmentCode0
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational InferenceCode0
TITAN: Bringing The Deep Image Prior to Implicit RepresentationsCode0
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation NetworksCode0
MR Slice Profile Estimation by Learning to Match Internal Patch DistributionsCode0
MRI Super-Resolution using Multi-Channel Total VariationCode0
Combination of Single and Multi-frame Image Super-resolution: An Analytical PerspectiveCode0
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection ModelsCode0
One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection ModelsCode0
A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering DrawingsCode0
To learn image super-resolution, use a GAN to learn how to do image degradation firstCode0
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural NetworksCode0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified