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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.
Efficient Residual Dense Block Search for Image Super-Resolution
Enhancing Traffic Scene Predictions with Generative Adversarial Networks
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme
Underwater Image Super-Resolution using Deep Residual Multipliers
Unsupervised Learning for Real-World Super-Resolution
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision
Edge-Informed Single Image Super-Resolution
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution
Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations
SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes
Robust Regression via Deep Negative Correlation Learning
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors
Learning Filter Basis for Convolutional Neural Network Compression
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
Image Formation Model Guided Deep Image Super-Resolution
Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution
Multi-Contrast Super-Resolution MRI Through a Progressive Network
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network
Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss
Benefiting from Multitask Learning to Improve Single Image Super-Resolution
Improved Super-Resolution Convolution Neural Network for Large Images
Learning Resolution-Invariant Deep Representations for Person Re-Identification
Image Super-Resolution Using a Wavelet-based Generative Adversarial Network
Progressive Perception-Oriented Network for Single Image Super-Resolution
Learned Image Downscaling for Upscaling using Content Adaptive Resampler
AVDNet: A Small-Sized Vehicle Detection Network for Aerial Visual Data
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
Single Image Super-Resolution via CNN Architectures and TV-TV Minimization
Hybrid Residual Attention Network for Single Image Super Resolution
Enhanced generative adversarial network for 3D brain MRI super-resolution
Gated Multiple Feedback Network for Image Super-Resolution
FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
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