Paper: https://arxiv.org/abs/2301.05187
Download image and occupancy examples from here. Place it in path/to/this/folder/data/
.
check requirements.txt
. This code was tested on python 3.8 in both Windows and Linux environments. Requirements file generated in Linux environment but should work similarly in a Windows environment.
Check wire_image_denoise.py
for denoise an image. We have included an example image from div2k in data/parrot.png
.
Check wire_occupancy.py
for fitting a 3D shape with occupancy information. For simplicity, we have included occupancy volume of Thai statue with regular sampling over 512x512x512 cube.
Check wire_multi_sr.py
for performing super-resolution with multiple images captured with small motion between the frames. We have included an example image data/kodak.png
for this script.
Please check wire_ct.py
for reconstructing images from computed tomographic measurements. We have included an example image data/chest.png
.
wire_image_denoise.py
: Runs training script for a single imagewire_occupancy.py
: Runs training script for a uniformly sampled occupancy of a 3D shapewire_multi_sr.py
: Runs training script for solving multi-image super-resolutionwire_ct.py
: Runs training script for solving the computed tomography problemrequirements.txt
: All requirements for running scripts in this foldermodules
: Contains functions to run the training scripts:
a. lin_inverse.py
: Includes forward operator for computed tomography
b. motion.py
: Contains functions for handling multi-image super resolution data generation
c. models.py
: Common wrapper to instantiate all INR models
d. utils.py
: Miscellaneous utilities
e. volutils.py
: Utilities for handling volume signals
f. wire.py
: Contains definitions for implementing WIRE.
g. wire2d.py
: Contains definition for 2d wire as shown in section 4.4 in the main paper.
h. mfn.py
: Contains multiplicative frequency networks implementation by the authors of the original paper
i. relu.py
: Contains relu and positional encoding implementation
j. siren.py
: Contains original implementation of SIREN
k. gauss.py
: Contains implementation of Gaussian nonlinearity
@inproceedings{saragadam2022wire,
title={WIRE: Wavelet Implicit Neural Representations},
author={Saragadam, Vishwanath and LeJeune, Daniel and Tan, Jasper and Balakrishnan, Guha and Veeraraghavan, Ashok and Baraniuk, Richard G},
booktitle={arXiv preprint arXiv:2301.05187}
year={2022}
}