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 1581–1590 of 3874 papers

TitleStatusHype
Image Restoration with Mean-Reverting Stochastic Differential EquationsCode2
Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows—0
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 imagesCode0
Simple diffusion: End-to-end diffusion for high resolution imagesCode2
Trainable Loss Weights in Super-ResolutionCode0
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution—0
AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics—0
Super-Resolution Harmonic Retrieval of Non-Circular Signals—0
Show:102550
← PrevPage 159 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified