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 3181–3190 of 3874 papers

TitleStatusHype
Deep learning at scale for subgrid modeling in turbulent flows—0
Super-resolution photoacoustic and ultrasound imaging with sparse arrays—0
Unsupervised Projection Networks for Generative Adversarial Networks—0
Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes—0
Frame and Feature-Context Video Super-Resolution—0
Learning to Have an Ear for Face Super-Resolution—0
Multi-grained Attention Networks for Single Image Super-Resolution—0
Lightweight Image Super-Resolution with Information Multi-distillation NetworkCode1
Analysis and Interpretation of Deep CNN Representations as Perceptual Quality Features—0
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified