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 2376–2400 of 3874 papers

TitleStatusHype
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping—0
G-PCC Post-Processing Using Fractional Super-Resolution—0
Online Video Super-Resolution with Convolutional Kernel Bypass Graft—0
H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System—0
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution—0
Pyramidal Denoising Diffusion Probabilistic Models—0
High Dynamic Range and Super-Resolution from Raw Image Bursts—0
Criteria Comparative Learning for Real-scene Image Super-ResolutionCode0
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression—0
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction—0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging—0
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
Improved Super Resolution of MR Images Using CNNs and Vision Transformers—0
Calcium oscillation on homogeneous and heterogeneous networks of ryanodine receptor—0
Semantic uncertainty intervals for disentangled latent spacesCode0
Efficient Meta-Tuning for Content-aware Neural Video DeliveryCode0
Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic ImagingCode0
HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images—0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Stochastic Attribute Modeling for Face Super-Resolution—0
Untrained, physics-informed neural networks for structured illumination microscopy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified