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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.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions
Semantic Segmentation Using Super Resolution Technique as Pre-Processing
SPDER: Semiperiodic Damping-Enabled Object Representation
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling
HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models
Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluation
TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes
A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-Resolution
Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models
Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images
Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution
Learning from Multi-Perception Features for Real-Word Image Super-resolution
High-Similarity-Pass Attention for Single Image Super-Resolution
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution
A Dive into SAM Prior in Image Restoration
Generalized Expectation Maximization Framework for Blind Image Super Resolution
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection
Efficient Mixed Transformer for Single Image Super-Resolution
Vehicle Detection and Classification without Residual Calculation: Accelerating HEVC Image Decoding with Random Perturbation Injection
Exploiting Diffusion Prior for Real-World Image Super-Resolution
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks
TelecomTM: A Fine-Grained and Ubiquitous Traffic Monitoring System Using Pre-Existing Telecommunication Fiber-Optic Cables as Sensors
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