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 2721–2730 of 3874 papers

TitleStatusHype
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature LearningCode1
Dense U-net for super-resolution with shuffle pooling layer—0
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets—0
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging—0
Joint Super-Resolution and Rectification for Solar Cell Inspection—0
EPSR: Edge Profile Super resolution—0
PAMS: Quantized Super-Resolution via Parameterized Max ScaleCode1
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified