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 726–750 of 3874 papers

TitleStatusHype
M&M VTO: Multi-Garment Virtual Try-On and EditingCode7
Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models—0
Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset ChallengeCode0
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential EquationsCode1
SuperFormer: Volumetric Transformer Architectures for MRI Super-ResolutionCode1
ReLUs Are Sufficient for Learning Implicit Neural RepresentationsCode1
Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image GenerationCode4
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism—0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
Imitating the Functionality of Image-to-Image Models Using a Single Example—0
W-Net: A Facial Feature-Guided Face Super-Resolution Network—0
SuperGaussian: Repurposing Video Models for 3D Super Resolution—0
Hybrid attention structure preserving network for reconstruction of under-sampled OCT images—0
CoNO: Complex Neural Operator for Continous Dynamical Physical Systems—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
SpikeMM: Flexi-Magnification of High-Speed Micro-Motions—0
Climate Variable Downscaling with Conditional Normalizing Flows—0
All-In-One Medical Image Restoration via Task-Adaptive RoutingCode2
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?—0
Blind Image Restoration via Fast Diffusion InversionCode3
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory MatchingCode2
Single image super-resolution based on trainable feature matching attention networkCode0
Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search—0
Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representationsCode0
Towards a Sampling Theory for Implicit Neural Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified