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

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Showing 141–150 of 3874 papers

TitleStatusHype
Integrated Super-resolution Sensing and Symbiotic Communication with 3D Sparse MIMO for Low-Altitude UAV Swarm—0
SupResDiffGAN a new approach for the Super-Resolution taskCode1
AnyTSR: Any-Scale Thermal Super-Resolution for UAVCode0
Event-Enhanced Blurry Video Super-ResolutionCode3
TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-ResolutionCode1
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and ResultsCode1
ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-ResolutionCode2
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified