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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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Multi-level Wavelet Convolutional Neural Networks
Distilling with Residual Network for Single Image Super Resolution
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution
Multi-Cue Vehicle Detection for Semantic Video Compression In Georegistered Aerial Videos
Densely Residual Laplacian Super-Resolution
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy
Hierarchical Back Projection Network for Image Super-Resolution
Exemplar Guided Face Image Super-Resolution without Facial Landmarks
On training deep networks for satellite image super-resolution
Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis
Unsupervised Image Noise Modeling with Self-Consistent GAN
Image-Adaptive GAN based Reconstruction
Hybrid Function Sparse Representation towards Image Super Resolution
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior
Suppressing Model Overfitting for Image Super-Resolution Networks
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Towards Real Scene Super-Resolution with Raw Images
Vehicle Detection in Deep Learning
An Intelligent Monitoring System of Vehicles on Highway Traffic
Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data
Training Object Detectors With Noisy Data
Medical image super-resolution method based on dense blended attention network
Ensemble Super-Resolution with A Reference Dataset
Handheld Multi-Frame Super-Resolution
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
SinGAN: Learning a Generative Model from a Single Natural Image
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-Net
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators
Multi-scale deep neural networks for real image super-resolution
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks
Tracking as A Whole: Multi-Target Tracking by Modeling Group Behavior with Sequential Detection
Forward Vehicle Collision Warning Based on Quick Camera Calibration
Automated Focal Loss for Image based Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.