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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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TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis
Deep Learning for Inverse Problems: Bounds and Regularizers
Medical Image Super-Resolution Using a Generative Adversarial Network
Progressive Image Deraining Networks: A Better and Simpler Baseline
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution
Vehicles Detection Based on Background Modeling
Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors
PVSS: A Progressive Vehicle Search System for Video Surveillance Networks
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
Image Super-Resolution as a Defense Against Adversarial Attacks
Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Coding
Residual Dense Network for Image Restoration
Multimodal Sensor Fusion In Single Thermal image Super-Resolution
Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network
Efficient Super Resolution Using Binarized Neural Network
Advanced Super-Resolution using Lossless Pooling Convolutional Networks
Wider Channel Attention Network for Remote Sensing Image Super-resolution
TuSeRACT: Turn-Sample-Based Real-Time Traffic Signal Control
Efficient Super Resolution For Large-Scale Images Using Attentional GAN
Unsupervised Degradation Learning for Single Image Super-Resolution
Supervised Deep Kriging for Single-Image Super-Resolution
The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery
Binary Document Image Super Resolution for Improved Readability and OCR Performance
Why Are Deep Representations Good Perceptual Quality Features?
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal Learning
MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
Image Reconstruction with Predictive Filter Flow
Deep Laplacian Pyramid Network for Text Images Super-Resolution
Joint Monocular 3D Vehicle Detection and Tracking
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
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