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 2651–2660 of 3874 papers

TitleStatusHype
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks—0
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration—0
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting—0
Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning—0
Boosting Optical Character Recognition: A Super-Resolution Approach—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution—0
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified