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 3381–3390 of 3874 papers

TitleStatusHype
Super-Resolution Off the Grid—0
Super resolution of histopathological frozen sections via deep learning preserving tissue structure—0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
A HVS-inspired Attention to Improve Loss Metrics for CNN-based Perception-Oriented Super-Resolution—0
Super-resolution of multispectral satellite images using convolutional neural networks—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
Super-resolution of periodic signals from short sequences of samples—0
Super-resolution of positive near-colliding point sources—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
ViTO: Vision Transformer-Operator—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified