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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.
Generative Adversarial Networks for Image Super-Resolution: A Survey
IMDeception: Grouped Information Distilling Super-Resolution Network
Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night
A New Dataset and Transformer for Stereoscopic Video Super-Resolution
Deep Model-Based Super-Resolution with Non-uniform Blur
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
NAFSSR: Stereo Image Super-Resolution Using NAFNet
Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
Self-Calibrated Efficient Transformer for Lightweight Super-Resolution
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
Cylin-Painting: Seamless {360\textdegree} Panoramic Image Outpainting and Beyond
GHM Wavelet Transform for Deep Image Super Resolution
Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking
Deep Vehicle Detection in Satellite Video
Dual-Stage Approach Toward Hyperspectral Image Super-Resolution
Mel-spectrogram features for acoustic vehicle detection and speed estimation
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution
An approach to improving sound-based vehicle speed estimation
Super-resolved multi-temporal segmentation with deep permutation-invariant networks
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder
Bayesian Image Super-Resolution with Deep Modeling of Image Statistics
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution
CenterLoc3D: Monocular 3D Vehicle Localization Network for Roadside Surveillance Cameras
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars
Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
Adaptive Patch Exiting for Scalable Single Image Super-Resolution
Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks
ARM: Any-Time Super-Resolution Method
HIPA: Hierarchical Patch Transformer for Single Image Super Resolution
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution
Image Super-Resolution With Deep Variational Autoencoders
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