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 2101–2110 of 3874 papers

TitleStatusHype
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense NetworkCode0
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization—0
StyleDEM: a Versatile Model for Authoring Terrains—0
Quantum Annealing for Single Image Super-Resolution—0
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation—0
Learning-based Framework for US Signals Super-resolution—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified