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 2276–2300 of 3874 papers

TitleStatusHype
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution—0
Generative Adversarial Networks for Image Super-Resolution: A Survey—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
A Generative Model for Generic Light Field Reconstruction—0
Generative Powers of Ten—0
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis—0
Generator From Edges: Reconstruction of Facial Images—0
Generic 3D Convolutional Fusion for image restoration—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction—0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
Geometry Enhancements from Visual Content: Going Beyond Ground Truth—0
GHM Wavelet Transform for Deep Image Super Resolution—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations—0
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution—0
Global graph features unveiled by unsupervised geometric deep learning—0
Global-Local Face Upsampling Network—0
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping—0
Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified