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 1671–1680 of 3874 papers

TitleStatusHype
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution—0
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation—0
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation—0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution—0
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution—0
Federated Learning for Blind Image Super-Resolution—0
Feature Super-Resolution: Make Machine See More Clearly—0
Show:102550
← PrevPage 168 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified