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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.
Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar Imaging
Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution Network
Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening
Robust and Fast Vehicle Detection using Augmented Confidence Map
Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditions
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense Network
Omni Aggregation Networks for Lightweight Image Super-Resolution
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
Quantum Annealing for Single Image Super-Resolution
Suspicious Vehicle Detection Using Licence Plate Detection And Facial Feature Recognition
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution
Cross-View Hierarchy Network for Stereo Image Super-Resolution
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input
Fast vehicle detection algorithm based on lightweight YOLO7-tiny
CrowdSim2: an Open Synthetic Benchmark for Object Detectors
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution
Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation
Real-time 6K Image Rescaling with Rate-distortion Optimization
Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration
Random Weights Networks Work as Loss Prior Constraint for Image Restoration
Implicit Diffusion Models for Continuous Super-Resolution
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution
Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators
Learning Generative Structure Prior for Blind Text Image Super-resolution
Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution
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