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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.
Incorporating Uncertainty-Guided and Top-k Codebook Matching for Real-World Blind Image Super-Resolution
GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution
Low-Complexity Super-Resolution Signature Estimation of XL-MIMO FMCW Radar
Task-driven real-world super-resolution of document scans
DM$^3$Net: Dual-Camera Super-Resolution via Domain Modulation and Multi-scale Matching
Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution
EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention
Enhancing Frequency for Single Image Super-Resolution with Learnable Separable Kernels
Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders
MARS: Radio Map Super-resolution and Reconstruction Method under Sparse Channel Measurements
DualX-VSR: Dual Axial Spatial$\times$Temporal Transformer for Real-World Video Super-Resolution without Motion Compensation
Multi-scale Image Super Resolution with a Single Auto-Regressive Model
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
Practical Manipulation Model for Robust Deepfake Detection
A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging
A Tree-guided CNN for image super-resolution
Application of convolutional neural networks in image super-resolution
A Survey of Deep Learning Video Super-Resolution
Omnidirectional Video Super-Resolution using Deep Learning
NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language Model
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance
Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey
VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics
Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution
Label-free Super-Resolution Microvessel Color Flow Imaging with Ultrasound
Burst Image Super-Resolution via Multi-Cross Attention Encoding and Multi-Scan State-Space Decoding
Deep Spectral Prior
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.