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 2071–2080 of 3874 papers

TitleStatusHype
Deep Image Super Resolution via Natural Image Priors—0
MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks—0
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution—0
Learning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution—0
Toward Super-Resolution for Appearance-Based Gaze Estimation—0
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding—0
Learning Parametric Sparse Models for Image Super-Resolution—0
Learning regularization and intensity-gradient-based fidelity for single image super resolution—0
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification—0
Learning Resolution-Invariant Deep Representations for Person Re-Identification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified