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 1701–1750 of 3874 papers

TitleStatusHype
AERO: Audio Super Resolution in the Spectral DomainCode2
SuperTran: Reference Based Video Transformer for Enhancing Low Bitrate Streams in Real Time—0
Rethinking Implicit Neural Representations for Vision Learners—0
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution—0
Blur Interpolation Transformer for Real-World Motion from BlurCode2
N-Gram in Swin Transformers for Efficient Lightweight Image Super-ResolutionCode1
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution—0
Guided Depth Super-Resolution by Deep Anisotropic DiffusionCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network—0
Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
Stereo Image Rain Removal via Dual-View Mutual Attention—0
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion ModuleCode1
Conffusion: Confidence Intervals for Diffusion ModelsCode1
RDRN: Recursively Defined Residual Network for Image Super-Resolution—0
Super-resolution Reconstruction of Single Image for Latent features—0
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution—0
Consistent Direct Time-of-Flight Video Depth Super-ResolutionCode1
CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming—0
MedleyVox: An Evaluation Dataset for Multiple Singing Voices SeparationCode1
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
Two-dimensional gridless super-resolution method for ISAR imaging—0
A Comprehensive Survey of Transformers for Computer Vision—0
Contrastive Learning for Climate Model Bias Correction and Super-Resolution—0
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data—0
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution NetworksCode1
Nondestructive thermographic detection of internal defects using pixel-pattern based laser excitation and photothermal super resolution reconstruction—0
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection—0
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report—0
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: ReportCode1
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems—0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact RemovalCode2
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Temporal Consistency Learning of inter-frames for Video Super-ResolutionCode0
VIINTER: View Interpolation with Implicit Neural Representations of Images—0
BUbble Flow Field: a Simulation Framework for Evaluating Ultrasound Localization Microscopy Algorithms—0
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
TITAN: Bringing The Deep Image Prior to Implicit RepresentationsCode0
Self-supervised Character-to-Character Distillation for Text RecognitionCode1
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical SystemsCode0
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
Data-Driven Computational Imaging for Scientific DiscoveryCode0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified