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 1291–1300 of 3874 papers

TitleStatusHype
A comparative analysis of SRGAN models—0
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)—0
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-ResolutionCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Surface Geometry Processing: An Efficient Normal-based Detail Representation—0
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-ResolutionCode1
MaxSR: Image Super-Resolution Using Improved MaxViT—0
Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework—0
Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational ImagingCode1
Show:102550
← PrevPage 130 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified