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 2611–2620 of 3874 papers

TitleStatusHype
Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior—0
Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows—0
Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution—0
Image Super-Resolution Using T-Tetromino Pixels—0
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution—0
Pixel-Level Kernel Estimation for Blind Super-ResolutionCode0
Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal SurveillanceCode0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention—0
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified