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
Sparse Coding Approach for Multi-Frame Image Super Resolution—0
Sparse Depth Super Resolution—0
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution—0
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning—0
Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos—0
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints—0
Sparsity-Based Super Resolution for SEM Images—0
Video Face Super-Resolution with Motion-Adaptive Feedback Cell—0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified