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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.
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging
Human Guided Ground-truth Generation for Realistic Image Super-resolution
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings
A High-Frequency Focused Network for Lightweight Single Image Super-Resolution
Parameter-Free Channel Attention for Image Classification and Super-Resolution
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images
SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution
Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution
Raising The Limit Of Image Rescaling Using Auxiliary Encoding
Recursive Generalization Transformer for Image Super-Resolution
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms
Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective
Single-photon Image Super-resolution via Self-supervised Learning
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
GRAN: Ghost Residual Attention Network for Single Image Super Resolution
TextIR: A Simple Framework for Text-based Editable Image Restoration
Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution
DISCO: Distributed Inference with Sparse Communications
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution
Improving Scene Text Image Super-resolution via Dual Prior Modulation Network
Likelihood Annealing: Fast Calibrated Uncertainty for Regression
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild
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