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 2341–2350 of 3874 papers

TitleStatusHype
MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused Multispectral Imagery—0
MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution—0
TriNeRFLet: A Wavelet Based Triplane NeRF Representation—0
MSRA-SR: Image Super-resolution Transformer with Multi-scale Shared Representation Acquisition—0
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution—0
Conditioning 3D Diffusion Models with 2D Images: Towards Standardized OCT Volumes through En Face-Informed Super-Resolution—0
Conditioned Regression Models for Non-Blind Single Image Super-Resolution—0
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution—0
Multi-Attributed and Structured Text-to-Face Synthesis—0
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
Show:102550
← PrevPage 235 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified