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 2321–2330 of 3874 papers

TitleStatusHype
Model reduction for the material point method via an implicit neural representation of the deformation map—0
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC AlgorithmCode1
DEM Super-Resolution with EfficientNetV2—0
TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs—0
Towards Representation Learning for Atmospheric DynamicsCode0
Simple and Efficient Unpaired Real-world Super-Resolution using Image Statistics—0
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data—0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification—0
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network—0
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical SystemsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified