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 721–730 of 3874 papers

TitleStatusHype
Towards Realistic Data Generation for Real-World Super-Resolution—0
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning—0
Binarized Diffusion Model for Image Super-ResolutionCode2
M2NO: Multiresolution Operator Learning with Multiwavelet-based Algebraic Multigrid Method—0
M&M VTO: Multi-Garment Virtual Try-On and EditingCode7
Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset ChallengeCode0
Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models—0
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential EquationsCode1
SuperFormer: Volumetric Transformer Architectures for MRI Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified