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 3031–3040 of 3874 papers

TitleStatusHype
Super Resolution Using Segmentation-Prior Self-Attention Generative Adversarial Network—0
Pixel-Level Self-Paced Learning for Super-ResolutionCode1
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model—0
Creating High Resolution Images with a Latent Adversarial GeneratorCode1
VESR-Net: The Winning Solution to Youku Video Enhancement and Super-Resolution Challenge—0
Turbulence Enrichment using Physics-informed Generative Adversarial Networks—0
Residual learning based densely connected deep dilated network for joint deblocking and super resolution—0
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better—0
Always Look on the Bright Side of the Field: Merging Pose and Contextual Data to Estimate Orientation of Soccer Players—0
Gated Fusion Network for Degraded Image Super ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified