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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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Lightweight image super-resolution with enhanced CNN
Extracting the fundamental diagram from aerial footage
Light Field Image Super-Resolution Using Deformable Convolution
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features
A deep primal-dual proximal network for image restoration
Cross-Scale Internal Graph Neural Network for Image Super-Resolution
HypervolGAN: An efficient approach for GAN with multi-objective training function
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping
Efficient Integer-Arithmetic-Only Convolutional Neural Networks
Real-World Super-Resolution via Kernel Estimation and Noise Injection
Progressively Unfreezing Perceptual GAN
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning
Exploring Sparsity in Image Super-Resolution for Efficient Inference
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
Data-Driven Based Tiny-YOLOv3 Method for Front Vehicle Detection Inducing SPP-Net
Neural Sparse Representation for Image Restoration
Learning Texture Transformer Network for Image Super-Resolution
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks
LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery
False Positive Removal for 3D Vehicle Detection with Penetrated Point Classifier
Perceptual Extreme Super Resolution Network with Receptive Field Block
Single Image Super-Resolution via Residual Neuron Attention Networks
Iterative Network for Image Super-Resolution
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
Invertible Image Rescaling
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
Scene Text Image Super-Resolution in the Wild
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
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