Awesome-Image-Transformation

This repo is a collection of AWESOME things about image transformation, including Super-Resolution； Image Completion；Image Style Transfer and Semantic-Segmentation. Feel free to star and fork.

Contents

• Papers
  • Super-Resolution
  • Image Completion
  • Image Style Transfer
  • Semantic Segmentation

Papers

Super-Resolution

• Learning a Deep Convolutional Network for Image Super-Resolution ECCV2014
• Image super-resolution using deep convolutional networks TPAMT2015
• Accurate image super-resolution using very deep convolutional networks CVPR2016
• Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network CVPR2016
• Perceptual Losses for Real-Time Style Transferand Super-Resolution ECCV2016
• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. CVPR2017
• SRFeat Single Image Super-Resolution with Feature Discrimination. ECCV2018

Image-Completion

• Context Encoders: Feature Learning by Inpainting. CVPR2016
• Semantic Image Inpainting with Deep Generative Models. CVPR2017
• High-Resolution Image Inpainting using Multi-Scale Neural Patch Systhesis. CVPR2017
• Globally and locally consistent image completion. TOG2017 Tensorflow-1 Tensorflow-2
• Generative image inpainting with contextual attention CVPR2018
• Image Inpainting for Irregular Holes Using Partial Convolutions. Arxiv-2018

Image-Style-Transfer

• A Neural Algorithm of Artistic Style. Arxiv-2015
• Image style transfer using convolutional neural networks.CVPR2016
• Perceptual Losses for Real-Time Style Transferand Super-Resolution. ECCV2016
• Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis. CVPR2016
• Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. ICCV2017
• Image-to-Image Translation with Conditional Adversarial Networks.CVPR2017
• Deep Photo Style Transfer. CVPR2017
• Unpaired image-to-image translation using cycle-consistent adversarial networks.ICCV2017
• Controlling perceptual factors in neural style transfer CVPR2017
• The Contextual Loss for Image Transformation with Non-Aligned Data ECCV2018
• Fast Patch-based Style Transfer of Arbitrary Style.

Semantic-Segmentation

• Fully convolutional networks for semantic segmentation.CVPR2015
• Learning deconvolution network for semantic segmentation. ICCV2015
• Semantic image segmentation with deep convolutional nets and fully connected crfs. ICLR2015
• U-net: Convolutional networks for biomedical image segmentation. MICCAI2015
• Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs.TPAMI2017
• Segnet: A deep convolutional encoder-decoder architecture for image segmentation.TPAMI2017

  GAN-Based

• Semantic Segmentation using Adversarial Networks. Arxiv-2016

Segmentation for Medical Image

• Adversarial training and dilated convolutions for brain MRI segmentation. Arxiv2017
• SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation. Neuroinformatics-2018