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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.
A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift
ESC-MISR: Enhancing Spatial Correlations for Multi-Image Super-Resolution in Remote Sensing
Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery
YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems
Fingerprints of Super Resolution Networks
Joint Audio-Visual Idling Vehicle Detection with Streamlined Input Dependencies
Deep Learning-Based CKM Construction with Image Super-Resolution
Learning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution
Guidance Disentanglement Network for Optics-Guided Thermal UAV Image Super-Resolution
Sebica: Lightweight Spatial and Efficient Bidirectional Channel Attention Super Resolution Network
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution
A Wavelet Diffusion GAN for Image Super-Resolution
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution
Spatiotemporal Object Detection for Improved Aerial Vehicle Detection in Traffic Monitoring
ConsisSR: Delving Deep into Consistency in Diffusion-based Image Super-Resolution
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution
HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution
Self-Supervised Learning for Real-World Object Detection: a Survey
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World Scenes
Distillation-Free One-Step Diffusion for Real-World Image Super-Resolution
Deep Learning Enhanced Road Traffic Analysis: Scalable Vehicle Detection and Velocity Estimation Using PlanetScope Imagery
Stochastic Deep Restoration Priors for Imaging Inverse Problems
Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration
Effective Diffusion Transformer Architecture for Image Super-Resolution
Finetuning YOLOv9 for Vehicle Detection: Deep Learning for Intelligent Transportation Systems in Dhaka, Bangladesh
A Generalized Tensor Formulation for Hyperspectral Image Super-Resolution Under General Spatial Blurring
Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-Resolution
LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs
NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results
Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity Clues
One Model for Two Tasks: Cooperatively Recognizing and Recovering Low-Resolution Scene Text Images by Iterative Mutual Guidance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.