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 1741–1750 of 3874 papers

TitleStatusHype
Degradation-Guided Meta-Restoration Network for Blind Super-Resolution—0
Binary Diffusion Probabilistic Model—0
Amortised MAP Inference for Image Super-resolution—0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—0
Deform-Mamba Network for MRI Super-Resolution—0
Binarized Neural Network for Single Image Super Resolution—0
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution—0
A Modular Conditional Diffusion Framework for Image Reconstruction—0
Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy—0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified