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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.
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EPSR: Edge Profile Super resolution
Symmetric Parallax Attention for Stereo Image Super-Resolution
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction
A Novel Fast 3D Single Image Super-Resolution Algorithm
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration
Differentiable Channel Sparsity Search via Weight Sharing within Filters
Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference
Satellite Image Small Target Application Based on Deep Segmented Residual Neural Network
Multi-Frame Super-Resolution Combining Demons Registration and Regularized Bayesian Reconstruction
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data
Attaining Real-Time Super-Resolution for Microscopic Images Using GAN
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors
Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution
Unfolding the Alternating Optimization for Blind Super Resolution
Non-anchor-based vehicle detection for traffic surveillance using bounding ellipses
LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
Efficient Image Super-Resolution Using Pixel Attention
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network
Lane Change For System-Driven Vehicles Using Dynamic Information
FAN: Frequency Aggregation Network for Real Image Super-resolution
Perceptual Modelling of Unconstrained Road Traffic Scenarios with Deep Learning
Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Tarsier: Evolving Noise Injection in Super-Resolution GANs
Deep Artifact-Free Residual Network for Single Image Super-Resolution
Blind Image Super-Resolution with Spatial Context Hallucination
Residual Feature Distillation Network for Lightweight Image Super-Resolution
MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion
GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes
AdderSR: Towards Energy Efficient Image Super-Resolution
Parallax Attention for Unsupervised Stereo Correspondence Learning
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