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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.
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network
Generative Powers of Ten
Infrared Image Super-Resolution via GAN
Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
Hypergraph Contrastive Learning for Drug Trafficking Community Detection
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution
DREAM: Diffusion Rectification and Estimation-Adaptive Models
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution
Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution
PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution
Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion Models
LFSRDiff: Light Field Image Super-Resolution via Diffusion Models
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
Image Super-Resolution with Text Prompt Diffusion
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution
Swift Parameter-free Attention Network for Efficient Super-Resolution
Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution
LATIS: Lambda Abstraction-based Thermal Image Super-resolution
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics
Scene Text Image Super-resolution based on Text-conditional Diffusion Models
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution
Texture and Noise Dual Adaptation for Infrared Image Super-Resolution
A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution
The Perception-Robustness Tradeoff in Deterministic Image Restoration
OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising Model
Efficient Model-Based Deep Learning via Network Pruning and Fine-Tuning
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
Exploring Deep Learning Image Super-Resolution for Iris Recognition
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction
Blind Image Super-resolution with Rich Texture-Aware Codebooks
Local Statistics for Generative Image Detection
Unpaired MRI Super Resolution with Contrastive Learning
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