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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.
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Perceptual Image Super-Resolution with Progressive Adversarial Network
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
Turbulence Enrichment using Physics-informed Generative Adversarial Networks
Creating High Resolution Images with a Latent Adversarial Generator
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model
Gated Fusion Network for Degraded Image Super Resolution
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better
Meta-Transfer Learning for Zero-Shot Super-Resolution
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data
Unpaired Image Super-Resolution using Pseudo-Supervision
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution
Facial Attribute Capsules for Noise Face Super Resolution
Generator From Edges: Reconstruction of Facial Images
Video Face Super-Resolution with Motion-Adaptive Feedback Cell
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution
SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud
Learning Deep Analysis Dictionaries for Image Super-Resolution
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries
On Learning Vehicle Detection in Satellite Video
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization
Multimodal Deep Unfolding for Guided Image Super-Resolution
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution
Learned Multi-View Texture Super-Resolution
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans
Characteristic Regularisation for Super-Resolving Face Images
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network
Exploiting Style and Attention in Real-World Super-Resolution
Scale-wise Convolution for Image Restoration
Spatial-Angular Interaction for Light Field Image Super-Resolution
Adaptive Densely Connected Super-Resolution Reconstruction
AeroRIT: A New Scene for Hyperspectral Image Analysis
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