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 3071–3080 of 3874 papers

TitleStatusHype
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations—0
Mosaic Super-resolution via Sequential Feature Pyramid Networks—0
Multi-modal Datasets for Super-resolution—0
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
Time accelerated image super-resolution using shallow residual feature representative network—0
Super-resolution of clinical CT volumes with modified CycleGAN using micro CT volumes—0
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified