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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 Super-Resolution with Guarantees via Conformalized Generative Models
Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric Priors
An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
DRiVE: Dynamic Recognition in VEhicles using snnTorch
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers
Exploring Linear Attention Alternative for Single Image Super-Resolution
Visual Autoregressive Modeling for Image Super-Resolution
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution
SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios
Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-Resolution
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration
LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision
Binary Diffusion Probabilistic Model
Automatic Labelling & Semantic Segmentation with 4D Radar Tensors
NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator Diffusion
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution
StructSR: Refuse Spurious Details in Real-World Image Super-Resolution
Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions
Combining YOLO and Visual Rhythm for Vehicle Counting
Research on vehicle detection based on improved YOLOv8 network
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution
Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)
MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO
SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos
Enhancing Nighttime Vehicle Detection with Day-to-Night Style Transfer and Labeling-Free Augmentation
Texture- and Shape-based Adversarial Attacks for Vehicle Detection in Synthetic Overhead Imagery
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution
Uncertainty Estimation for Super-Resolution using ESRGAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.