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 3726–3750 of 3874 papers

TitleStatusHype
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network—0
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging—0
A recommender system to restore images with impulse noise—0
Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning—0
Pixel Recursive Super ResolutionCode0
Language Independent Single Document Image Super-Resolution using CNN for improved recognition—0
Super-resolution Using Constrained Deep Texture Synthesis—0
Dual Recovery Network with Online Compensation for Image Super-Resolution—0
Joint Dictionary Learning for Example-based Image Super-resolution—0
Light Field Super-Resolution Via Graph-Based Regularization—0
Learning a Mixture of Deep Networks for Single Image Super-Resolution—0
Super-Resolution Reconstruction of Electrical Impedance Tomography Images—0
EnhanceNet: Single Image Super-Resolution Through Automated Texture SynthesisCode0
Super-resolution photoacoustic microscopy using blind structured illumination—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network—0
Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution—0
Learning Parametric Sparse Models for Image Super-Resolution—0
Multi-resolution Data Fusion for Super-Resolution Electron Microscopy—0
The World of Fast Moving ObjectsCode0
Multiframe Motion Coupling for Video Super ResolutionCode0
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRICode0
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold SimplificationCode0
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified