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 3811–3820 of 3874 papers

TitleStatusHype
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledgeCode0
A Fusion-Guided Inception Network for Hyperspectral Image Super-ResolutionCode0
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
Super-Efficient Super Resolution for Fast Adversarial Defense at the EdgeCode0
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality AssessmentCode0
Fine-grained Urban Flow Inference with Incomplete DataCode0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimationCode0
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From ShadingCode0
Sampling Theory for Super-Resolution with Implicit Neural RepresentationsCode0
Show:102550
← PrevPage 382 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified