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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.
EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration
Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer
DPSRGAN: Dilation Patch Super-Resolution Generative Adversarial Networks
Good Practices and A Strong Baseline for Traffic Anomaly Detection
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution
Infrared Image Super-Resolution via Transfer Learning and PSRGAN
Enabling 3D Object Detection with a Low-Resolution LiDAR
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain Connectivity
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models
Incident Detection on Junctions Using Image Processing
Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View
SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation
A Two-Stage Attentive Network for Single Image Super-Resolution
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution
Kernel Adversarial Learning for Real-world Image Super-resolution
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS
Attention in Attention Network for Image Super-Resolution
Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes
BAM: A Balanced Attention Mechanism for Single Image Super Resolution
Image Super-Resolution via Iterative Refinement
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging
Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations
Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection
Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography
Best-Buddy GANs for Highly Detailed Image Super-Resolution
Flow-based Kernel Prior with Application to Blind Super-Resolution
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution
Training a Task-Specific Image Reconstruction Loss
Asymmetric CNN for image super-resolution
Multi-frame Super-resolution from Noisy Data
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network
A new public Alsat-2B dataset for single-image super-resolution
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings
RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection
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