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 2271–2280 of 3874 papers

TitleStatusHype
Blind inverse problems with isolated spikes—0
TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle ImagingCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
FREGAN : an application of generative adversarial networks in enhancing the frame rate of videos—0
AdaPool: Exponential Adaptive Pooling for Information-Retaining DownsamplingCode1
A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded—0
Learning Continuous Representation of Audio for Arbitrary Scale Super ResolutionCode1
Functional Neural Networks for Parametric Image Restoration Problems—0
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timeCode0
Scale-Aware Dynamic Network for Continuous-Scale Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified