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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.
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning
Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
Single Image Super-Resolution Using Lightweight CNN with Maxout Units
Generative Adversarial Networks: An Overview
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
Fast Vehicle Detection in Aerial Imagery
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving
Deep Mean-Shift Priors for Image Restoration
Deep multi-frame face super-resolution
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network
Joint Maximum Purity Forest with Application to Image Super-Resolution
Fast single image super-resolution based on sigmoid transformation
MemNet: A Persistent Memory Network for Image Restoration
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
Enhanced Deep Residual Networks for Single Image Super-Resolution
Towards lightweight convolutional neural networks for object detection
High-Quality Face Image SR Using Conditional Generative Adversarial Networks
Multi-frame image super-resolution with fast upscaling technique
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
Face Super-Resolution Through Wasserstein GANs
A Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips
Single Image Super-Resolution based on Wiener Filter in Similarity Domain
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Learning Deep CNN Denoiser Prior for Image Restoration
Detail-revealing Deep Video Super-resolution
Improving Vision-based Self-positioning in Intelligent Transportation Systems via Integrated Lane and Vehicle Detection
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