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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.
Identifying High Accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-Based Method
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Distilling the Knowledge from Conditional Normalizing Flows
Fairness for Image Generation with Uncertain Sensitive Attributes
One-to-many Approach for Improving Super-Resolution
A Framework for Real-time Traffic Trajectory Tracking, Speed Estimation, and Driver Behavior Calibration at Urban Intersections Using Virtual Traffic Lanes
Feedback Pyramid Attention Networks for Single Image Super-Resolution
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing
Variational AutoEncoder for Reference based Image Super-Resolution
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising
Fourier Space Losses for Efficient Perceptual Image Super-Resolution
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Self-Organized Residual Blocks for Image Super-Resolution
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding
A Dataset for Provident Vehicle Detection at Night
Permutation invariance and uncertainty in multitemporal image super-resolution
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
High-Frequency aware Perceptual Image Enhancement
MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond
Content-adaptive Representation Learning for Fast Image Super-resolution
Anchor-based Plain Net for Mobile Image Super-Resolution
XCycles Backprojection Acoustic Super-Resolution
Improved detection of small objects in road network sequences
FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness
A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution
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