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 581–590 of 3874 papers

TitleStatusHype
RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-ExemplarsCode1
Fast Nearest Convolution for Real-Time Efficient Image Super-ResolutionCode1
HST: Hierarchical Swin Transformer for Compressed Image Super-resolutionCode1
Learning Degradation Representations for Image DeblurringCode1
Hard-Constrained Deep Learning for Climate DownscalingCode1
Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolutionCode1
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-ResolutionCode1
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object DetectionCode1
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGANCode1
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified