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 1941–1950 of 3874 papers

TitleStatusHype
When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study—0
Joint Super-Resolution and Rectification for Solar Cell Inspection—0
Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt—0
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution—0
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Kalman-Inspired Feature Propagation for Video Face Super-Resolution—0
Deep Sampling Networks—0
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution—0
Deep Residual Axial Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified