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 1251–1260 of 3874 papers

TitleStatusHype
Data-driven Super-Resolution of Flood Inundation Maps using Synthetic SimulationsCode0
Data-Driven Computational Imaging for Scientific DiscoveryCode0
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range InteractionsCode0
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface GenerationCode0
DaBiT: Depth and Blur informed Transformer for Joint Refocusing and Super-ResolutionCode0
Machine learning for reconstruction of polarity inversion lines from solar filamentsCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approachCode0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified