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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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Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
Neural Differential Equations for Single Image Super-resolution
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-Attention
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations
Probabilistic Oriented Object Detection in Automotive Radar
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks
Learning A Single Network for Scale-Arbitrary Super-Resolution
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution
Deep Adaptive Inference Networks for Single Image Super-Resolution
Image super-resolution reconstruction based on attention mechanism and feature fusion
Time accelerated image super-resolution using shallow residual feature representative network
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
Arbitrary Scale Super-Resolution for Brain MRI Images
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces
Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
DHP: Differentiable Meta Pruning via HyperNetworks
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
Structure-Preserving Super Resolution with Gradient Guidance
Learning regularization and intensity-gradient-based fidelity for single image super resolution
Deep Unfolding Network for Image Super-Resolution
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
Across-scale Process Similarity based Interpolation for Image Super-Resolution
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention
High-Resolution Daytime Translation Without Domain Labels
Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
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