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Super-Resolution
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Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SpectraLift: Physics-Guided Spectral-Inversion Network for Self-Supervised Hyperspectral Image Super-Resolution
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
4KAgent: Agentic Any Image to 4K Super-Resolution
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns
Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution
STAR-Pose: Efficient Low-Resolution Video Human Pose Estimation via Spatial-Temporal Adaptive Super-Resolution
R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution
Compressed Video Super-Resolution based on Hierarchical Encoding
Exploring Diffusion with Test-Time Training on Efficient Image Restoration
Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
DoA Estimation using MUSIC with Range/Doppler Multiplexing for MIMO-OFDM Radar
ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations
Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution
On the Natural Robustness of Vision-Language Models Against Visual Perception Attacks in Autonomous Driving
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution
Joint Angle and Velocity-Estimation for Target Localization in Bistatic mmWave MIMO Radar in the Presence of Clutter
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models
Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution
ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing
Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales
ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems
Sampling Theory for Super-Resolution with Implicit Neural Representations
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration Models
LiftVSR: Lifting Image Diffusion to Video Super-Resolution via Hybrid Temporal Modeling with Only 4$\times$RTX 4090s
MAMBO: High-Resolution Generative Approach for Mammography Images
Grids Often Outperform Implicit Neural Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.