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 2211–2220 of 3874 papers

TitleStatusHype
First order algorithms in variational image processing—0
FlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation—0
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation—0
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers—0
FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring—0
Show:102550
← PrevPage 222 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified