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 2626–2650 of 3874 papers

TitleStatusHype
Perceptually-inspired super-resolution of compressed videos—0
Perceptually Optimized Super Resolution—0
Performance Boundaries and Tradeoffs in Super-Resolution Imaging Technologies for Space Targets—0
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression—0
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing—0
PFStorer: Personalized Face Restoration and Super-Resolution—0
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution—0
PG-DPIR: An efficient plug-and-play method for high-count Poisson-Gaussian inverse problems—0
PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction—0
Phase Retrieval using Untrained Neural Network Priors—0
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation—0
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution—0
Bridging high resolution sub-cellular imaging with physiologically relevant engineered tissues—0
Photorealistic Video Generation with Diffusion Models—0
Perceptual Video Super Resolution with Enhanced Temporal Consistency—0
Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging—0
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation—0
Physics-informed deep-learning applications to experimental fluid mechanics—0
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation—0
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations—0
Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models—0
Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images—0
Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics—0
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified