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 1141–1150 of 3874 papers

TitleStatusHype
Deep Residual Network for Joint Demosaicing and Super-ResolutionCode0
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-ResolutionCode0
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
Unsupervised Hyperspectral and Multispectral Image Fusion via Self-Supervised Modality DecouplingCode0
Deep learning Framework for Mobile MicroscopyCode0
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold SimplificationCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Mining self-similarity: Label super-resolution with epitomic representationsCode0
MetH: A family of high-resolution and variable-shape image challengesCode0
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problemsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified