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 1431–1440 of 3874 papers

TitleStatusHype
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense NetworkCode0
Omni Aggregation Networks for Lightweight Image Super-ResolutionCode2
Revisiting Implicit Neural Representations in Low-Level VisionCode1
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization—0
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and ResultsCode1
StyleDEM: a Versatile Model for Authoring Terrains—0
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
Quantum Annealing for Single Image Super-Resolution—0
Learning-based Framework for US Signals Super-resolution—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified