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 1771–1780 of 3874 papers

TitleStatusHype
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks—0
A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution—0
Deep Learning based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering—0
Lightweight Stepless Super-Resolution of Remote Sensing Images via Saliency-Aware Dynamic Routing StrategyCode1
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows—0
ISTA-Inspired Network for Image Super-Resolution—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
CUF: Continuous Upsampling Filters—0
Action Matching: Learning Stochastic Dynamics from SamplesCode1
QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified