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 3591–3600 of 3874 papers

TitleStatusHype
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial NetworksCode0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing ImagesCode0
Distortion-aware super-resolution for planetary exploration imagesCode0
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep LearningCode0
Spatially-Variant Degradation Model for Dataset-free Super-resolutionCode0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral ImageryCode0
Blind Image Fusion for Hyperspectral Imaging with the Directional Total VariationCode0
Quasi-supervised Learning for Super-resolution PETCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified