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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.
StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representation
A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution
Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance
Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution
Efficient Vision-based Vehicle Speed Estimation
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution
Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network
GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information
Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study
DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution
NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results
ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior
TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-Resolution
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
PatrolVision: Automated License Plate Recognition in the wild
Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution
Enhanced Semantic Extraction and Guidance for UGC Image Super Resolution
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution
PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution
BUFF: Bayesian Uncertainty Guided Diffusion Probabilistic Model for Single Image Super-Resolution
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior
Reliable Traffic Monitoring Using Low-Cost Doppler Radar Units
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images Only
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution
Deterministic Medical Image Translation via High-fidelity Brownian Bridges
Progressive Focused Transformer for Single Image Super-Resolution
$L^2$FMamba: Lightweight Light Field Image Super-Resolution with State Space Model
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment
Single-Step Latent Consistency Model for Remote Sensing Image Super-Resolution
Burst Image Super-Resolution with Mamba
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model
Event-Based Crossing Dataset (EBCD)
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