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 2631–2640 of 3874 papers

TitleStatusHype
SRFeat: Single Image Super-Resolution with Feature Discrimination—0
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments—0
SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations—0
SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution—0
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data—0
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks—0
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution—0
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution—0
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging—0
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified