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 1861–1870 of 3874 papers

TitleStatusHype
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping—0
Learning Degradation Representations for Image DeblurringCode1
G-PCC Post-Processing Using Fractional Super-Resolution—0
Hard-Constrained Deep Learning for Climate DownscalingCode1
Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolutionCode1
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-ResolutionCode1
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object DetectionCode1
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGANCode1
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-ResolutionCode1
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified