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 721–730 of 3874 papers

TitleStatusHype
Deep Burst Super-ResolutionCode1
Cross-Scale Internal Graph Neural Network for Image Super-ResolutionCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-ResolutionCode1
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time seriesCode1
Iterative Soft Shrinkage Learning for Efficient Image Super-ResolutionCode1
Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal LearningCode1
Cross-View Hierarchy Network for Stereo Image Super-ResolutionCode1
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
Diffusion Prior Interpolation for Flexibility Real-World Face Super-ResolutionCode1
Show:102550
← PrevPage 73 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified