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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
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Generic 3D Convolutional Fusion for image restoration
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
Deep Cascaded Bi-Network for Face Hallucination
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior
RAISR: Rapid and Accurate Image Super Resolution
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Image Super-Resolution Based on Sparsity Prior via Smoothed $l_0$ Norm
Con-Patch: When a Patch Meets its Context
Patch-Ordering as a Regularization for Inverse Problems in Image Processing
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization
Matrix Neural Networks
Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study
Matrix Variate RBM and Its Applications
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection
Double Sparse Multi-Frame Image Super Resolution
Fidelity-Naturalness Evaluation of Single Image Super Resolution
Super-Resolution with Deep Convolutional Sufficient Statistics
Deeply-Recursive Convolutional Network for Image Super-Resolution
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Seven ways to improve example-based single image super resolution
Diverse Large-Scale ITS Dataset Created from Continuous Learning for Real-Time Vehicle Detection
Fast Single Image Super-Resolution
Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples
Is Image Super-resolution Helpful for Other Vision Tasks?
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
Deep Networks for Image Super-Resolution with Sparse Prior
Boosting Optical Character Recognition: A Super-Resolution Approach
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
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