WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution

Related tags

Deep LearningWPPNets
Overview

WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution

This code belongs to the paper [1] available at https://arxiv.org/abs/2201.08157. Please cite the paper, if you use this code.

The paper [1] is The repository contains an implementation of WPPNets as introduced in [1]. It contains scripts for reproducing the numerical example Texture superresolution in Section 5.2.

Moreover, the file wgenpatex.py is adapted from [2] available at https://github.com/johertrich/Wasserstein_Patch_Prior and is adapted from [3]. Furthermore, the folder model is adapted from [5] available at https://github.com/hellloxiaotian/ACNet.

The folders test_img and training_img contain parts of the textures from [4].

For questions and bug reports, please contact Fabian Altekrueger (fabian.altekrueger(at)hu-berlin.de).

CONTENTS

  1. REQUIREMENTS
  2. USAGE AND EXAMPLES
  3. REFERENCES

1. REQUIREMENTS

The code requires several Python packages. We tested the code with Python 3.9.7 and the following package versions:

  • pytorch 1.10.0
  • matplotlib 3.4.3
  • numpy 1.21.2
  • pykeops 1.5

Usually the code is also compatible with some other versions of the corresponding Python packages.

2. USAGE AND EXAMPLES

You can start the training of the WPPNet by calling the scripts. If you want to load the existing network, please set retrain to False. Checkpoints are saved automatically during training such that the progress of the reconstructions is observable. Feel free to vary the parameters and see what happens.

TEXTURE GRASS

The script run_grass.py is the implementation of the superresolution example in [1, Section 5.2] for the Kylberg Texture [4] grass which is available at https://kylberg.org/kylberg-texture-dataset-v-1-0. The high-resolution ground truth and the reference image are different 600×600 sections cropped from the original texture images. Similarly, the low-resolution training data is generated by cropping 100×100 sections from the texture images, artificially downsampling it by a predefined forward operator f and adding Gaussian noise. For more details on the downsampling process, see [1, Section 5.2].

TEXTURE FLOOR

The script run_floor.py is the implementation of the superresolution example in [1, Section 5.2] for the Kylberg Texture [4] Floor which is available at https://kylberg.org/kylberg-texture-dataset-v-1-0. The high-resolution ground truth and the reference image are different 600×600 sections cropped from the original texture images. Similarly, the low-resolution training data is generated by cropping 100×100 sections from the texture images, artificially downsampling it by a predefined forward operator f and adding Gaussian noise. For more details on the downsampling process, see [1, Section 5.2].

3. REFERENCES

[1] F. Altekrueger, J. Hertrich.
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution.
ArXiv Preprint#2201.08157

[2] J. Hertrich, A. Houdard and C. Redenbach.
Wasserstein Patch Prior for Image Superresolution.
ArXiv Preprint#2109.12880

[3] A. Houdard, A. Leclaire, N. Papadakis and J. Rabin.
Wasserstein Generative Models for Patch-based Texture Synthesis.
ArXiv Preprint#2007.03408

[4] G. Kylberg.
The Kylberg texture dataset v. 1.0.
Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, 2011

[5] C. Tian, Y. Xu, W. Zuo, C.-W. Lin, and D. Zhang.
Asymmetric CNN for image superresolution.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021.

Owner
Fabian Altekrueger
Fabian Altekrueger
DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene.

DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene. We achieve NeRF-comparable novel-view synthesis quality with super-fast convergence.

sunset 709 Dec 31, 2022
A proof of concept ai-powered Recaptcha v2 solver

Recaptcha Fullauto I've decided to open source my old Recaptcha v2 solver. My latest version will be opened sourced this summer. I am hoping this proj

Nate 60 Dec 20, 2022
Project code for weakly supervised 3D object detectors using wide-baseline multi-view traffic camera data: WIBAM.

WIBAM (Work in progress) Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data 3D object dete

Matthew Howe 10 Aug 24, 2022
MarcoPolo is a clustering-free approach to the exploration of bimodally expressed genes along with group information in single-cell RNA-seq data

MarcoPolo is a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering Overview MarcoPolo

Chanwoo Kim 13 Dec 18, 2022
pytorch implementation of fast-neural-style

fast-neural-style 🌇 🚀 NOTICE: This codebase is no longer maintained, please use the codebase from pytorch examples repository available at pytorch/e

Abhishek Kadian 405 Dec 15, 2022
This dlib-based facial login system

Facial-Login-System This dlib-based facial login system is a technology capable of matching a human face from a digital webcam frame capture against a

Mushahid Ali 3 Apr 23, 2022
Experiments with Fourier layers on simulation data.

Factorized Fourier Neural Operators This repository contains the code to reproduce the results in our NeurIPS 2021 ML4PS workshop paper, Factorized Fo

Alasdair Tran 57 Dec 25, 2022
PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Don’t be Contradicted with Anything!CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System This repository contains the PyTorch im

Libo Qin 25 Sep 06, 2022
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features

PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features Overview This repository is the Pytorch implementation of PRIN/SPRIN: On Extracting P

Yang You 17 Mar 02, 2022
Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022)

Blockwise Sequential Model Learning Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022) For ins

2 Jun 17, 2022
A particular navigation route using satellite feed and can help in toll operations & traffic managemen

How about adding some info that can quanitfy the stress on a particular navigation route using satellite feed and can help in toll operations & traffic management The current analysis is on the satel

Ashish Pandey 1 Feb 14, 2022
LBK 20 Dec 02, 2022
Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems"

Code Artifacts Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driv

Andrea Stocco 2 Aug 24, 2022
Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation

Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation Our paper is accepted by ICCV2021. Picture: Overview of the proposed Plug-an

Yunfei Liu 32 Dec 10, 2022
Lightweight library to build and train neural networks in Theano

Lasagne Lasagne is a lightweight library to build and train neural networks in Theano. Its main features are: Supports feed-forward networks such as C

Lasagne 3.8k Dec 29, 2022
Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad to your characters in Modo.

Applicator Kit for Modo Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad with a TrueDepth camera to

Andrew Buttigieg 3 Aug 24, 2021
Medical Insurance Cost Prediction using Machine earning

Medical-Insurance-Cost-Prediction-using-Machine-learning - Here in this project, I will use regression analysis to predict medical insurance cost for people in different regions, and based on several

1 Dec 27, 2021
[ICCV'21] UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction Project Page | Paper | Supplementary | Video This reposit

331 Dec 28, 2022
Use CLIP to represent video for Retrieval Task

A Straightforward Framework For Video Retrieval Using CLIP This repository contains the basic code for feature extraction and replication of results.

Jesus Andres Portillo Quintero 54 Dec 22, 2022
Spline is a tool that is capable of running locally as well as part of well known pipelines like Jenkins (Jenkinsfile), Travis CI (.travis.yml) or similar ones.

Welcome to spline - the pipeline tool Important note: Since change in my job I didn't had the chance to continue on this project. My main new project

Thomas Lehmann 29 Aug 22, 2022