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 181–190 of 3874 papers

TitleStatusHype
PLAIN: Scalable Estimation Architecture for Integrated Sensing and CommunicationCode0
Diffusion Image Prior—0
Residual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking—0
Progressive Focused Transformer for Single Image Super-ResolutionCode2
Consistency Trajectory Matching for One-Step Generative Super-Resolution—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
ESSR: An 8K@30FPS Super-Resolution Accelerator With Edge Selective Network—0
Burst Image Super-Resolution with Mamba—0
Single-Step Latent Consistency Model for Remote Sensing Image Super-Resolution—0
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality AssessmentCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified