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 981–990 of 3874 papers

TitleStatusHype
Holopix50k: A Large-Scale In-the-wild Stereo Image DatasetCode1
ML-SIM: A deep neural network for reconstruction of structured illumination microscopy imagesCode1
Deep Unfolding Network for Image Super-ResolutionCode1
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector NetworkCode1
Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal LearningCode1
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial LearningCode1
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
Closed-loop Matters: Dual Regression Networks for Single Image Super-ResolutionCode1
Learning Enriched Features for Real Image Restoration and EnhancementCode1
Show:102550
← PrevPage 99 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified