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 851–875 of 3874 papers

TitleStatusHype
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual LossCode2
Super-Resolution Analysis for Landfill Waste Classification—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRFCode1
Video Interpolation with Diffusion Models—0
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
DRCT: Saving Image Super-resolution away from Information BottleneckCode3
Exploiting Self-Supervised Constraints in Image Super-ResolutionCode1
SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising—0
Burst Super-Resolution with Diffusion Models for Improving Perceptual QualityCode1
Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D—0
Ship in Sight: Diffusion Models for Ship-Image Super ResolutionCode1
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network—0
Masked Autoencoders are PDE LearnersCode0
Climate Downscaling: A Deep-Learning Based Super-resolution Model of Precipitation Data with Attention Block and Skip Connections—0
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion ModelCode2
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution MicroscopyCode0
A Study in Dataset Pruning for Image Super-Resolution—0
Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-ResolutionCode0
Residual Dense Swin Transformer for Continuous Depth-Independent Ultrasound ImagingCode1
LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)—0
CFAT: Unleashing TriangularWindows for Image Super-resolutionCode2
Adaptive Super Resolution For One-Shot Talking-Head GenerationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified