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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.
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution
Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology
Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution
A Benchmark for Chinese-English Scene Text Image Super-resolution
Dual Aggregation Transformer for Image Super-Resolution
Exploring the Physical World Adversarial Robustness of Vehicle Detection
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution
Fully $1\times1$ Convolutional Network for Lightweight Image Super-Resolution
StarSRGAN: Improving Real-World Blind Super-Resolution
Robust Detection, Association, and Localization of Vehicle Lights: A Context-Based Cascaded CNN Approach and Evaluations
ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution
Patterns of Vehicle Lights: Addressing Complexities in Curation and Annotation of Camera-Based Vehicle Light Datasets and Metrics
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting
NLCUnet: Single-Image Super-Resolution Network with Hairline Details
On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement
PartDiff: Image Super-resolution with Partial Diffusion Models
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network
Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement
Learned Scalable Video Coding For Humans and Machines
Soft-IntroVAE for Continuous Latent space Image Super-Resolution
Knowledge Distillation for Object Detection: from generic to remote sensing datasets
A comparative analysis of SRGAN models
Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-Resolution
DARTS: Double Attention Reference-based Transformer for Super-resolution
MaxSR: Image Super-Resolution Using Improved MaxViT
DWA: Differential Wavelet Amplifier for Image Super-Resolution
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution
Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection
Spatio-Temporal Perception-Distortion Trade-off in Learned Video SR
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution
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