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 1876–1900 of 3874 papers

TitleStatusHype
High Dynamic Range and Super-Resolution from Raw Image Bursts—0
GLEAN: Generative Latent Bank for Image Super-Resolution and BeyondCode5
Meta-Learning based Degradation Representation for Blind Super-ResolutionCode1
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
Criteria Comparative Learning for Real-scene Image Super-ResolutionCode0
NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT CamerasCode1
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression—0
Reference-based Image Super-Resolution with Deformable Attention TransformerCode1
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction—0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging—0
Calcium oscillation on homogeneous and heterogeneous networks of ryanodine receptor—0
Improved Super Resolution of MR Images Using CNNs and Vision Transformers—0
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
Towards Interpretable Video Super-Resolution via Alternating OptimizationCode1
Deep Audio Waveform PriorCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Semantic uncertainty intervals for disentangled latent spacesCode0
Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic ImagingCode0
Efficient Meta-Tuning for Content-aware Neural Video DeliveryCode0
HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images—0
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Image Super-Resolution with Deep DictionaryCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified