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Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
논문
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Generic Perceptual Loss for Modeling Structured Output Dependencies
Collapsible Linear Blocks for Super-Efficient Super Resolution
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image
Real-World Single Image Super-Resolution: A Brief Review
Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement
Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution
A Comprehensive Review of Deep Learning-based Single Image Super-resolution
Selfie Periocular Verification using an Efficient Super-Resolution Approach
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images
A Low Cost Modular Radio Tomography System for Bicycle and Vehicle Detection and Classification
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images
Deep learning architectural designs for super-resolution of noisy images
Single Image Super-Resolution using Residual Channel Attention Network
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling
Real-World Super-Resolution of Face-Images from Surveillance Cameras
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products
Exploiting Raw Images for Real-Scene Super-Resolution
Deep Burst Super-Resolution
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-Resolution
Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution
Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views
Progressive Image Super-Resolution via Neural Differential Equation
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution
Deep Learning-based Face Super-Resolution: A Survey
Single Image Super-Resolution
More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination
ISETAuto: Detecting vehicles with depth and radiance information
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