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 2701–2725 of 3874 papers

TitleStatusHype
Pre-Trained Image Processing TransformerCode1
DeFMO: Deblurring and Shape Recovery of Fast Moving ObjectsCode1
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning—0
Model Adaptation for Inverse Problems in Imaging—0
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images—0
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images—0
Fully Quantized Image Super-Resolution NetworksCode1
Rank-One Network: An Effective Framework for Image RestorationCode1
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution—0
FireSRnet: Geoscience-Driven Super-Resolution of Future Fire Risk from Climate Change—0
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—0
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition—0
On-Device Text Image Super Resolution—0
Robust super-resolution depth imaging via a multi-feature fusion deep networkCode1
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution—0
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments—0
Assessing Wireless Sensing Potential with Large Intelligent Surfaces—0
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature LearningCode1
Dense U-net for super-resolution with shuffle pooling layer—0
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets—0
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging—0
Show:102550
← PrevPage 109 of 155Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified