Detail-Preserving Transformer for Light Field Image Super-Resolution

Related tags

Deep Learning DPT
Overview

DPT

Official Pytorch implementation of the paper "Detail-Preserving Transformer for Light Field Image Super-Resolution" accepted by AAAI 2022 .

Updates

  • 2022.01: Our method is available at the newly-released repository BasicLFSR, an open-source and easy-to-use toolbox for LF image SR.
  • 2022.01: The code is released.

Requirements

  • Python 3.7.7
  • Pytorch=1.5.0
  • torchvision=0.6.0
  • h5py=2.8.0
  • Matlab

Dataset

We use the EPFL, HCInew, HCIold, INRIA and STFgantry datasets for both training and testing. You can download the above dataset from Baidu Drive (key:912V).

Download the visual results

We share the super-resolved results generated by our DPT. Then, researchers can compare their methods to our DPT without performing inference. Results are available at Baidu Drive (key:912V).

Prepare the datasets

To generate the training data,

 Using Matlab to run `GenerateTrainingData.m`

To generate the testing data,

 Using Matlab to run `GenerateTestData.m`

We also provide the processed datasets we used in the paper. The processed datasets are avaliable at Baidu Drive (key:912V).

Train

To perform DPT training, please run

python train.py

Checkpoint will be saved to ./log/.

Test

To evaluate DPT performance, please run

python test.py

The performance of DPT on five datasets will be printed on the screen. The visual result of each scene will be saved in ./Results/. The PSNR and SSIM values of each scene will aslo be saved in ./PSNRSSIM/.

Generate visual results

To generate the visual super-resolved results,

Using Matlab to run `GenerateResultImages.m` 

The '.mat' files in ./Results/ will be converted to '.png' images to ./SRimages/.

To generate the visual gradient results, please run

python generate_visual_gradient_map.py 

Gradient results will be saved to ./GRAimages/.

Citation

If you find this work helpful, please consider citing the following paper:

@article{wang2022detail,
  title={Detail Preserving Transformer for Light Field Image Super-Resolution},
  author={Wang, Shunzhou and Zhou, Tianfei and Lu, Yao and Di, Huijun},
  journal={arXiv preprint arXiv:2201.00346},
  year={2022}
}

Acknowledgements

This code is heavily based on LF-DFNet. We also refer to the codes in VSR-Transformer, COLA-Net, and SPSR. We thank the authors for sharing the codes. We would like to thank Yingqian Wang for his help with LFSR. We would also like to thank Zhengyu Liang for adding our DPT to the repository BasicLFSR.

Contact

If you have any question about this work, feel free to concat with me via [email protected].

You might also like...
A framework for joint super-resolution and image synthesis, without requiring real training data
A framework for joint super-resolution and image synthesis, without requiring real training data

SynthSR This repository contains code to train a Convolutional Neural Network (CNN) for Super-resolution (SR), or joint SR and data synthesis. The met

Repository for
Repository for "Exploring Sparsity in Image Super-Resolution for Efficient Inference", CVPR 2021

SMSR Reposity for "Exploring Sparsity in Image Super-Resolution for Efficient Inference" [arXiv] Highlights Locate and skip redundant computation in S

MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution (CVPR2021)

MASA-SR Official PyTorch implementation of our CVPR2021 paper MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Re

PyTorch code for our paper "Attention in Attention Network for Image Super-Resolution"

Under construction... Attention in Attention Network for Image Super-Resolution (A2N) This repository is an PyTorch implementation of the paper "Atten

PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.

GCResNet PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021. The code will

PyTorch code for our paper
PyTorch code for our paper "Image Super-Resolution with Non-Local Sparse Attention" (CVPR2021).

Image Super-Resolution with Non-Local Sparse Attention This repository is for NLSN introduced in the following paper "Image Super-Resolution with Non-

PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network"

HAN PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network" This repository is for HAN introduced in the

Implementation of paper:
Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch

SRDenseNet-pytorch Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch (http://openaccess.thecvf.com/content_ICC

[ACM MM 2021] Joint Implicit Image Function for Guided Depth Super-Resolution
[ACM MM 2021] Joint Implicit Image Function for Guided Depth Super-Resolution

Joint Implicit Image Function for Guided Depth Super-Resolution This repository contains the code for: Joint Implicit Image Function for Guided Depth

Comments
  • download processed datasets

    download processed datasets

    Hi, I want to download processed datasets and other files from thislink but I can't do that without signing up. Could you please share these files with google drive or suggest any solution for download?

    opened by givkashi 3
Owner
null
Activating More Pixels in Image Super-Resolution Transformer

HAT [Paper Link] Activating More Pixels in Image Super-Resolution Transformer Xiangyu Chen, Xintao Wang, Jiantao Zhou and Chao Dong BibTeX @article{ch

XyChen 270 Dec 27, 2022
Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021) This repository is the official P

Jingyun Liang 159 Dec 30, 2022
Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021) This repository is the official P

Jingyun Liang 159 Dec 30, 2022
Code release for Local Light Field Fusion at SIGGRAPH 2019

Local Light Field Fusion Project | Video | Paper Tensorflow implementation for novel view synthesis from sparse input images. Local Light Field Fusion

null 1.1k Dec 27, 2022
Code release for Local Light Field Fusion at SIGGRAPH 2019

Local Light Field Fusion Project | Video | Paper Tensorflow implementation for novel view synthesis from sparse input images. Local Light Field Fusion

null 748 Nov 27, 2021
This is the official implementation code repository of Underwater Light Field Retention : Neural Rendering for Underwater Imaging (Accepted by CVPR Workshop2022 NTIRE)

Underwater Light Field Retention : Neural Rendering for Underwater Imaging (UWNR) (Accepted by CVPR Workshop2022 NTIRE) Authors: Tian Ye†, Sixiang Che

jmucsx 17 Dec 14, 2022
This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021

Deep-Detail-Enhancement-for-Any-Garment Introduction This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in

null 40 Dec 13, 2022
Implementation detail for paper "Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet"

Multi-level-colonoscopy-malignant-tissue-detection-with-adversarial-CAC-UNet Implementation detail for our paper "Multi-level colonoscopy malignant ti

CVSM Group -  email: czhu@bupt.edu.cn 84 Nov 22, 2022
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset (CVPR2022)

FaceVerse FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset Lizhen Wang, Zhiyuan Chen, Tao Yu, Chenguang

Lizhen Wang 219 Dec 28, 2022