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

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Showing 1251–1275 of 3874 papers

TitleStatusHype
A Self-Supervised Deep Denoiser for Hyperspectral and Multispectral Image FusionCode0
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems—0
HSD-PAM: High Speed Super Resolution Deep Penetration Photoacoustic Microscopy Imaging Boosted by Dual Branch Fusion Network—0
Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-ResolutionCode1
PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data—0
Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging—0
When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study—0
A Benchmark for Chinese-English Scene Text Image Super-resolutionCode1
Dual Aggregation Transformer for Image Super-ResolutionCode2
NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing FrameworkCode1
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage—0
DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation—0
One-stage Low-resolution Text Recognition with High-resolution Knowledge TransferCode1
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-ResolutionCode1
Super-Resolution Estimation of UWB Channels including the Diffuse Component -- An SBL-Inspired Approach—0
Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point CloudsCode1
Efficient neural supersampling on a novel gaming dataset—0
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution—0
Lightweight Super-Resolution Head for Human Pose EstimationCode1
Fully 11 Convolutional Network for Lightweight Image Super-ResolutionCode1
StarSRGAN: Improving Real-World Blind Super-ResolutionCode1
RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid SubjectsCode0
Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architectureCode0
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth EstimationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified