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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.
Blockwise Parallel Decoding for Deep Autoregressive Models
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network
Bi-GANs-ST for Perceptual Image Super-resolution
Neural Nearest Neighbors Networks
Vehicle Tracking Using Surveillance with Multimodal Data Fusion
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution
Channel Splitting Network for Single MR Image Super-Resolution
Deep Learning-Based Channel Estimation
Deep Bi-Dense Networks for Image Super-Resolution
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN
Triple Attention Mixed Link Network for Single Image Super Resolution
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution
Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields
Kernel based low-rank sparse model for single image super-resolution
Addressing Training Bias via Automated Image Annotation
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network
The 2018 PIRM Challenge on Perceptual Image Super-resolution
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical Rectification
Generative adversarial network-based image super-resolution using perceptual content losses
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
Deep MR Image Super-Resolution Using Structural Priors
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network
Learning Sparse Low-Precision Neural Networks With Learnable Regularization
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Wide Activation for Efficient and Accurate Image Super-Resolution
Efficient Single Image Super Resolution using Enhanced Learned Group Convolutions
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