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 2941–2950 of 3874 papers

TitleStatusHype
Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses—0
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Unsupervised Projection Networks for Generative Adversarial Networks—0
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective—0
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation—0
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation—0
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution—0
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples—0
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified