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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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Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
Explorable Super Resolution
Texture Hallucination for Large-Factor Painting Super-Resolution
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers
PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-Resolution
Deep Decomposition Learning for Inverse Imaging Problems
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution
Convolutional Neural Network-based Optical Camera Communication System for Internet of Vehicles
Fine-Grained Neural Architecture Search
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
Frequency Separation for Real-World Super-Resolution
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks
A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imaging
AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results
Training Set Effect on Super Resolution for Automated Target Recognition
Self-supervised Moving Vehicle Tracking with Stereo Sound
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation
Vehicle detection and counting from VHR satellite images: efforts and open issues
Component Attention Guided Face Super-Resolution Network: CAGFace
Image Restoration Using Deep Regulated Convolutional Networks
Multimodal Image Super-resolution via Deep Unfolding with Side Information
Image Super-Resolution via Attention based Back Projection Networks
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion
Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution
Unsupervised Image Super-Resolution with an Indirect Supervised Path
Image Super-Resolution Improved by Edge Information
Bio-Inspired Foveated Technique for Augmented-Range Vehicle Detection Using Deep Neural Networks
Unsupervised Projection Networks for Generative Adversarial Networks
Lightweight Image Super-Resolution with Information Multi-distillation Network
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