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

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Showing 3776–3800 of 3874 papers

TitleStatusHype
Game Theory for Adversarial Attacks and DefensesCode0
RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid SubjectsCode0
Audio Super Resolution using Neural NetworksCode0
xUnit: Learning a Spatial Activation Function for Efficient Image RestorationCode0
GAMBAS: Generalised-Hilbert Mamba for Super-resolution of Paediatric Ultra-Low-Field MRICode0
Attention Based Real Image RestorationCode0
Attention-based Multi-Reference Learning for Image Super-ResolutionCode0
Robust Deep Ensemble Method for Real-world Image DenoisingCode0
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface GenerationCode0
Deep Learning for Cornea Microscopy Blind DeblurringCode0
Zoomed In, Diffused Out: Towards Local Degradation-Aware Multi-Diffusion for Extreme Image Super-ResolutionCode0
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution SegmentationCode0
Accelerating the Training of Video Super-Resolution ModelsCode0
Sub-frame Appearance and 6D Pose Estimation of Fast Moving ObjectsCode0
FSRNet: End-to-End Learning Face Super-Resolution with Facial PriorsCode0
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing FlowCode0
Frequency Separation for Real-World Super-ResolutionCode0
Deep learning-based super-resolution fluorescence microscopy on small datasetsCode0
FOD-Swin-Net: angular super resolution of fiber orientation distribution using a transformer-based deep modelCode0
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-ResolversCode0
Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation DecodersCode0
Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic ImagingCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
ContrastiveGaussian: High-Fidelity 3D Generation with Contrastive Learning and Gaussian SplattingCode0
3DSRnet: Video Super-resolution using 3D Convolutional Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified