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 2791–2800 of 3874 papers

TitleStatusHype
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets—0
Surveillance Face Anti-spoofing—0
SwiftSRGAN -- Rethinking Super-Resolution for Efficient and Real-time Inference—0
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge—0
SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting—0
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis—0
Synthesis of realistic fetal MRI with conditional Generative Adversarial Networks—0
Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach—0
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture—0
Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified