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 791–800 of 3874 papers

TitleStatusHype
Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change ProjectionsCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
AdaPool: Exponential Adaptive Pooling for Information-Retaining DownsamplingCode1
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from FacesCode1
Deep Audio Waveform PriorCode1
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC AlgorithmCode1
Meta-SR: A Magnification-Arbitrary Network for Super-ResolutionCode1
Meta-Transfer Learning for Zero-Shot Super-ResolutionCode1
DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaCode1
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified