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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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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios
R$^3$-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos
Deep Learning for Single Image Super-Resolution: A Brief Review
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer Learning
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution
To learn image super-resolution, use a GAN to learn how to do image degradation first
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
Perceptual Video Super Resolution with Enhanced Temporal Consistency
An Attention-Based Approach for Single Image Super Resolution
DroNet: Efficient convolutional neural network detector for real-time UAV applications
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Detecting Tiny Moving Vehicles in Satellite Videos
Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis
CT-image Super Resolution Using 3D Convolutional Neural Network
Non-Local Recurrent Network for Image Restoration
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution
A hybrid approach of interpolations and CNN to obtain super-resolution
Multi-level Wavelet-CNN for Image Restoration
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution
Image Super-Resolution via Dual-State Recurrent Networks
Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks
Large Receptive Field Networks for High-Scale Image Super-Resolution
Densely Connected High Order Residual Network for Single Frame Image Super Resolution
Learning Warped Guidance for Blind Face Restoration
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
A two-stage 3D Unet framework for multi-class segmentation on full resolution image
An efficient CNN for spectral reconstruction from RGB images
Reference-Conditioned Super-Resolution by Neural Texture Transfer
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
A Fully Progressive Approach to Single-Image Super-Resolution
POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors
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