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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.
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution
A Study in Dataset Pruning for Image Super-Resolution
CFAT: Unleashing TriangularWindows for Image Super-resolution
Vehicle Detection Performance in Nordic Region
Efficient scene text image super-resolution with semantic guidance
Bounding Box Stability against Feature Dropout Reflects Detector Generalization across Environments
VmambaIR: Visual State Space Model for Image Restoration
CasSR: Activating Image Power for Real-World Image Super-Resolution
PAON: A New Neuron Model using Padé Approximants
Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction
Activating Wider Areas in Image Super-Resolution
Efficient Diffusion Model for Image Restoration by Residual Shifting
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution
XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolution
EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
Text-guided Explorable Image Super-resolution
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts
CAMixerSR: Only Details Need More "Attention"
SeD: Semantic-Aware Discriminator for Image Super-Resolution
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution
Real-Time Vehicle Detection and Urban Traffic Behavior Analysis Based on UAV Traffic Videos on Mobile Devices
Generative AI in Vision: A Survey on Models, Metrics and Applications
Adaptive Convolutional Neural Network for Image Super-resolution
MambaIR: A Simple Baseline for Image Restoration with State-Space Model
Generative Adversarial Models for Extreme Geospatial Downscaling
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.