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 3751–3760 of 3874 papers

TitleStatusHype
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
E-FCNN for tiny facial expression recognition—0
Generalizable One-shot Neural Head Avatar—0
EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention—0
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Generalized Matrix-Pencil Approach to Estimation of Complex Exponentials with Gapped Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified