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 41–50 of 3874 papers

TitleStatusHype
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-ResolutionCode0
Practical Manipulation Model for Robust Deepfake DetectionCode0
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-ResolutionCode0
Multi-scale Image Super Resolution with a Single Auto-Regressive Model—0
EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention—0
Enhancing Frequency for Single Image Super-Resolution with Learnable Separable Kernels—0
MARS: Radio Map Super-resolution and Reconstruction Method under Sparse Channel Measurements—0
Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation DecodersCode0
DualX-VSR: Dual Axial SpatialTemporal Transformer for Real-World Video Super-Resolution without Motion Compensation—0
A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imagingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified