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 1176–1200 of 3874 papers

TitleStatusHype
Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution HeadCode1
Multi-Depth Branch Network for Efficient Image Super-ResolutionCode1
Effect of structure-based training on 3D localization precision and quality—0
Neural Operators for Accelerating Scientific Simulations and Design—0
Guided Frequency Loss for Image Restoration—0
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution SegmentationCode0
Uncertainty Quantification via Neural Posterior Principal Components—0
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion ModelsCode1
An Ensemble Model for Distorted Images in Real Scenarios—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
A Lightweight Recurrent Grouping Attention Network for Video Super-ResolutionCode0
Data Upcycling Knowledge Distillation for Image Super-ResolutionCode0
Adaptation of the super resolution SOTA for Art Restoration in camera capture imagesCode0
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data—0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
License Plate Super-Resolution Using Diffusion Models—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
Self-Supervised Super-Resolution Approach for Isotropic Reconstruction of 3D Electron Microscopy Images from Anisotropic Acquisition—0
Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approachCode0
Universal Photorealistic Style Transfer: A Lightweight and Adaptive Approach—0
Frequency Estimation Using Complex-Valued Shifted Window TransformerCode1
Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-ResolutionCode1
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified