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 3091–3100 of 3874 papers

TitleStatusHype
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database—0
Characteristic Regularisation for Super-Resolving Face Images—0
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks—0
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution—0
CNN-generated images are surprisingly easy to spot... for nowCode1
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—0
Rapid Whole-Heart CMR with Single Volume Super-resolution—0
Exploiting Style and Attention in Real-World Super-Resolution—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
Anisotropic Super Resolution in Prostate MRI using Super Resolution Generative Adversarial Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified