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 1401–1410 of 3874 papers

TitleStatusHype
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images—0
Data-Driven Design for Fourier Ptychographic Microscopy—0
A HVS-inspired Attention to Improve Loss Metrics for CNN-based Perception-Oriented Super-Resolution—0
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU—0
A High-Frequency Focused Network for Lightweight Single Image Super-Resolution—0
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network—0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified