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 1501–1510 of 3874 papers

TitleStatusHype
Super Resolution On Global Weather Forecasts—0
Single-Layer Learnable Activation for Implicit Neural Representation (SL^2A-INR)—0
NSSR-DIL: Null-Shot Image Super-Resolution Using Deep Identity Learning—0
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
Learning Two-factor Representation for Magnetic Resonance Image Super-resolution—0
Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution—0
Adversarial Deep-Unfolding Network for MA-XRF Super-Resolution on Old Master Paintings Using Minimal Training Data—0
Test-time Training for Hyperspectral Image Super-resolution—0
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition—0
Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified