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 1991–2000 of 3874 papers

TitleStatusHype
Joint OAM Radar-Communication Systems: Target Recognition and Beam Optimization—0
RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch NormalizationCode0
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds—0
Accelerating the Training of Video Super-Resolution ModelsCode0
MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-ResolutionCode1
Activating More Pixels in Image Super-Resolution TransformerCode3
A Real Time Super Resolution Accelerator with Tilted Layer Fusion—0
Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Images—0
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-ResolutionCode1
Private Eye: On the Limits of Textual Screen Peeking via Eyeglass Reflections in Video Conferencing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified