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 2261–2270 of 3874 papers

TitleStatusHype
GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution—0
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention—0
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution—0
Gemino: Practical and Robust Neural Compression for Video Conferencing—0
GenDR: Lightning Generative Detail Restorator—0
Generalizable One-shot Neural Head Avatar—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Generalized Matrix-Pencil Approach to Estimation of Complex Exponentials with Gapped Data—0
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified