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 1891–1900 of 3874 papers

TitleStatusHype
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution—0
Facial Expression Restoration Based on Improved Graph Convolutional Networks—0
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention—0
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography—0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Facial Attribute Capsules for Noise Face Super Resolution—0
Is There Any Recovery Guarantee with Coupled Structured Matrix Factorization for Hyperspectral Super-Resolution?—0
Is There Tradeoff between Spatial and Temporal in Video Super-Resolution?—0
Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution—0
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation—0
Show:102550
← PrevPage 190 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified