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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
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
Interpretable Deep Multimodal Image Super-Resolution
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution
Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS
Image Super-Resolution using Explicit Perceptual Loss
MDCN: Multi-scale Dense Cross Network for Image Super-Resolution
Multi-Attention Based Ultra Lightweight Image Super-Resolution
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors
Deep Variational Network Toward Blind Image Restoration
Cascade Convolutional Neural Network for Image Super-Resolution
PNEN: Pyramid Non-Local Enhanced Networks
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations
Single Image Super-Resolution via a Holistic Attention Network
DAWN: Vehicle Detection in Adverse Weather Nature Dataset
Transfer Learning for Protein Structure Classification at Low Resolution
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
Component Divide-and-Conquer for Real-World Image Super-Resolution
Sub-Pixel Back-Projection Network For Lightweight Single Image Super-Resolution
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution
Utilising Visual Attention Cues for Vehicle Detection and Tracking
Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners
What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks
Multi-Step Reinforcement Learning for Single Image Super-Resolution
Frequency Domain-based Perceptual Loss for Super Resolution
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution
Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network
Learning with Privileged Information for Efficient Image Super-Resolution
EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery
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