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 2021–2030 of 3874 papers

TitleStatusHype
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution—0
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT SimulationCode0
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank ApproximationCode0
SPDER: Semiperiodic Damping-Enabled Object Representation—0
Cutting-Edge Techniques for Depth Map Super-Resolution—0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing—0
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging SystemsCode0
Iterative-in-Iterative Super-Resolution Biomedical Imaging Using One Real Image—0
Store and Fetch Immediately: Everything Is All You Need for Space-Time Video Super-resolutionCode0
Show:102550
← PrevPage 203 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified