Solid Harmonic Wavelet Bispectrum for Image Analysis

Wed, 03 Dec 2025 17:14:00 +0000

Kymatio

Tue, 02 Apr 2019 13:37:24 +0200

Kymatio is a Python module for computing wavelet and scattering transforms.

It is built on top of PyTorch, but also has a fast CUDA backend via cupy and skcuda.

Use kymatio if you need a library that:

integrates wavelet scattering in a deep learning architecture,
supports 1-D, 2-D, and 3-D wavelets, and runs seamlessly on CPU and GPU hardware.
A brief intro to wavelet scattering is provided in User Guide. For a list of publications see Publications.

Quick Start

On Linux or macOS, open a shell and run the instruction of [kymatio](https://github.com/kymatio/kymatio).

In the Python intepreter, you may then call:

import kymatio

which should run without error if the package has been correctly installed.

Apply 2D scattering to a 32x32 random image

The following code imports ```torch``` and the ```Scattering2D``` class, which implements the 2D scattering transform. It then creates an instance of this class to compute the scattering transform at scale ```J = 2``` of a ```32x32``` image consisting of Gaussian white noise:

import torch
from kymatio import Scattering2D
scattering = Scattering2D(J=2, shape=(32, 32))
x = torch.randn(1, 1, 32, 32)
Sx = scattering(x)
print(Sx.size())

This should output:

torch.Size([1, 1, 81, 8, 8])

This corresponds to 81 scattering coefficients, each corresponding to an 8x8 image.

Check out the User Guide for more scattering transform examples.

Kymatio: Scattering Transforms in Python

Fri, 28 Dec 2018 00:00:00 +0000

Solid harmonic wavelet scattering for predictions of molecule properties

Thu, 28 Jun 2018 00:00:00 +0000

Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities

Tue, 21 Nov 2017 00:00:00 +0000