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 811–820 of 3874 papers

TitleStatusHype
AutoGAN-Distiller: Searching to Compress Generative Adversarial NetworksCode1
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-ResolutionCode1
Dual-Camera Super-Resolution with Aligned Attention ModulesCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-ResolutionCode1
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark EstimationCode1
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstructionCode1
Neural Side-by-Side: Predicting Human Preferences for No-Reference Super-Resolution EvaluationCode1
DREAM: Diffusion Rectification and Estimation-Adaptive ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified