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 3021–3030 of 3874 papers

TitleStatusHype
Pyramidal Edge-maps and Attention based Guided Thermal Super-resolution—0
Is There Tradeoff between Spatial and Temporal in Video Super-Resolution?—0
W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM MappingCode1
Memory-efficient Learning for Large-scale Computational Imaging—0
Deep Blind Video Super-resolutionCode1
Hierarchical Neural Architecture Search for Single Image Super-ResolutionCode1
Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds—0
FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale Context Aggregation and Feature Space Super-resolution—0
Perceptual Image Super-Resolution with Progressive Adversarial Network—0
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative ModelsCode3
Show:102550
← PrevPage 303 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified