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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.
Image-specific Convolutional Kernel Modulation for Single Image Super-resolution
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution
Towards Live Video Analytics with On-Drone Deeper-yet-Compatible Compression
GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention
Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation
Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art
Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution
Scale-Aware Dynamic Network for Continuous-Scale Super-Resolution
Turning Traffic Monitoring Cameras into Intelligent Sensors for Traffic Density Estimation
Localized Super Resolution for Foreground Images using U-Net and MR-CNN
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation
Improving Super-Resolution Performance using Meta-Attention Layers
RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network
Dense Dual-Attention Network for Light Field Image Super-Resolution
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution
Error-correcting neural networks for semi-Lagrangian advection in the level-set method
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform
Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models
ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution
In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission
Boosting Lightweight Single Image Super-resolution via Joint-distillation
Learning Meta-path-aware Embeddings for Recommender Systems
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging
EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
Burst Image Restoration and Enhancement
ParaDiS: Parallelly Distributable Slimmable Neural Networks
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution
A Systematic Survey of Deep Learning-based Single-Image Super-Resolution
Comparison of Object Detection Algorithms Using Video and Thermal Images Collected from a UAS Platform: An Application of Drones in Traffic Management
Structure-Preserving Image Super-Resolution
Vehicle Detection and Tracking From Surveillance Cameras in Urban Scenes
Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving
Resolution based Feature Distillation for Cross Resolution Person Re-Identification
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