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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.
Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation
Variational Deep Image Restoration
Exploring the solution space of linear inverse problems with GAN latent geometry
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution
Multi-Modality Image Super-Resolution using Generative Adversarial Networks
Real-World Single Image Super-Resolution Under Rainy Condition
Hypernetwork-Based Adaptive Image Restoration
Real-World Light Field Image Super-Resolution via Degradation Modulation
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps
Robust Deep Ensemble Method for Real-world Image Denoising
Patch-based image Super Resolution using generalized Gaussian mixture model
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution
Real-World Image Super-Resolution by Exclusionary Dual-Learning
Real-Time Super-Resolution for Real-World Images on Mobile Devices
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net
Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution
Image Super-resolution with An Enhanced Group Convolutional Neural Network
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
LiVeR: Lightweight Vehicle Detection and Classification in Real-Time
Diverse super-resolution with pretrained deep hiererarchical VAEs
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling
Semantically Accurate Super-Resolution Generative Adversarial Networks
Residual Local Feature Network for Efficient Super-Resolution
Blueprint Separable Residual Network for Efficient Image Super-Resolution
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Activating More Pixels in Image Super-Resolution Transformer
Private Eye: On the Limits of Textual Screen Peeking via Eyeglass Reflections in Video Conferencing
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion
C3-STISR: Scene Text Image Super-resolution with Triple Clues
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