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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.
Toward task-driven satellite image super-resolution
CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution
The Power of Context: How Multimodality Improves Image Super-Resolution
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution
Rethinking Image Evaluation in Super-Resolution
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network
RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling
C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales
TriLiteNet: Lightweight Model for Multi-Task Visual Perception
QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution
Perceive, Understand and Restore: Real-World Image Super-Resolution with Autoregressive Multimodal Generative Models
FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution
Dual-domain Modulation Network for Lightweight Image Super-Resolution
RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification
MegaSR: Mining Customized Semantics and Expressive Guidance for Image Super-Resolution
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution
CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios
Emulating Self-attention with Convolution for Efficient Image Super-Resolution
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution
Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution
AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual Learning
Geodesic Diffusion Models for Medical Image-to-Image Generation
MFSR-GAN: Multi-Frame Super-Resolution with Handheld Motion Modeling
BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery
Automatic Vehicle Detection using DETR: A Transformer-Based Approach for Navigating Treacherous Roads
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution
MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge Distillation
Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations
Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing
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