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 3651–3660 of 3874 papers

TitleStatusHype
Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution from a Blurred Image Sequence—0
Deep Mean-Shift Priors for Image RestorationCode0
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach—0
Deep multi-frame face super-resolution—0
Benchmarking Super-Resolution Algorithms on Real Data—0
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration—0
Joint Maximum Purity Forest with Application to Image Super-ResolutionCode0
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network—0
Sparsity-Based Super Resolution for SEM Images—0
Fast single image super-resolution based on sigmoid transformation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified