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 1451–1475 of 3874 papers

TitleStatusHype
Multi Kernel Estimation based Object SegmentationCode0
Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion StrategiesCode0
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models—0
Advanced Underwater Image Quality Enhancement via Hybrid Super-Resolution Convolutional Neural Networks and Multi-Scale Retinex-Based Defogging Techniques—0
ConsisSR: Delving Deep into Consistency in Diffusion-based Image Super-Resolution—0
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation—0
MMAD-Purify: A Precision-Optimized Framework for Efficient and Scalable Multi-Modal Attacks—0
Super-resolving Real-world Image Illumination Enhancement: A New Dataset and A Conditional Diffusion ModelCode0
Transformer based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches—0
Spatio-Temporal Distortion Aware Omnidirectional Video Super-ResolutionCode0
Optimizing Fingerprint-Spectrum-Based Synchronization in Integrated Sensing and Communications—0
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution—0
REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI SegmentationCode0
HASN: Hybrid Attention Separable Network for Efficient Image Super-resolutionCode0
Conditioning 3D Diffusion Models with 2D Images: Towards Standardized OCT Volumes through En Face-Informed Super-Resolution—0
Super Resolution Based on Deep Operator Networks—0
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities—0
Co-learning Single-Step Diffusion Upsampler and Downsampler with Two Discriminators and Distillation—0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Near-Field ISAC in 6G: Addressing Phase Nonlinearity via Lifted Super-Resolution—0
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results—0
Exploring Strengths and Weaknesses of Super-Resolution Attack in Deepfake Detection—0
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns—0
TV-based Deep 3D Self Super-Resolution for fMRI—0
Stochastic Deep Restoration Priors for Imaging Inverse Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified