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 2751–2760 of 3874 papers

TitleStatusHype
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model—0
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception—0
Quantum Annealing for Single Image Super-Resolution—0
Quasi-Newton OMP Approach for Super-Resolution Channel Estimation and Extrapolation—0
Unsupervised Domain Adaptation for Neuron Membrane Segmentation based on Structural Features—0
Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Dataset—0
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified