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 1551–1560 of 3874 papers

TitleStatusHype
Fingerprints of Super Resolution Networks—0
Fingerprinting Deep Image Restoration Models—0
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery—0
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
Geometry Enhancements from Visual Content: Going Beyond Ground Truth—0
A Coordinate Descent Approach to Atomic Norm Denoising—0
2D Neural Fields with Learned Discontinuities—0
Hybrid Neural Representations for Spherical Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified