how to implement integer wavelet transform (lifting wavelet transform)

[Manoj Pandey]

Hello Everyone,

I am a PhD scholar and i want to implement lifting wavelet transform using python so please send me code or guide me how to do the same

Thanks 

[Manoj Pandey]

Dear All,

i am trying to implement LWT of an image and i got code from https://pypi.org/project/ctwavelet/ 

Codes are:

from ctwavelet import LiftingScheme
ls = LiftingScheme.LiftingScheme()
ls.apply([0,1,2,3,4,5,6,7])

coeffs = ls.get_wavelet_coeffs()
print(coeffs)

inverse_coeffs = ls.inverse(coeffs)
print(inverse_coeffs)

but while i am running this code in a Jupyter notebook it is giving the error 'LiftingScheme' object has no attribute 'get_wavelet_coeffs'

i am not able to debug this error please help me out.

Thanks in advance

Manoj Pandey

PhD Research Scholar 

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Thanks & Regards

Manoj Kumar Pandey

PhD  Research Scholar 

GGU, Bilaspur 

[Alenrex Maity]

from ctwavelet import LiftingScheme
ls = LiftingScheme.LiftingScheme()
ls.apply([0,1,2,3,4,5,6,7])

coeffs = ls.get_wavelet_coefficients()

print(coeffs)
inverse_coeffs = ls.inverse(coeffs)
print(inverse_coeffs)

[Liviu Vladutu]

Hi

Instead of that try

coeffs = ls.get_wavelet_coefficients()
print(coeffs)

and it will work. 

Rgds,

Liviu Vladutu, PhD, MSc, BSc (Honors)

https://sites.google.com/site/liviuvladutu/home

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[Deepu]

Implementing the lifting wavelet transform using Python involves several steps. Here's a general outline of the process:

1. Import the necessary libraries:
```python
import numpy as np
```

2. Define the lifting steps:
The lifting wavelet transform consists of a series of lifting steps. Each lifting step involves prediction and update operations. Define functions to perform these operations.

```python
def predict_update(input_data):
    # Perform prediction
    predicted = input_data[1::2] - 0.5 * (input_data[::2] + input_data[2::2])
 
    # Perform update
    updated = input_data[::2] + 0.25 * (predicted[:-1] + predicted[1:])
 
    return predicted, updated
```

3. Implement the lifting wavelet transform:
Write a function that applies the lifting steps iteratively to perform the wavelet transform.

```python
def lifting_wavelet_transform(input_data, num_steps):
    # Initialize the output array
    output_data = np.copy(input_data)
 
    # Perform the specified number of lifting steps
    for _ in range(num_steps):
        predicted, updated = predict_update(output_data)
     
        # Update the output data with the predicted and updated values
        output_data[::2] = updated
        output_data[1::2] = predicted
 
    return output_data
```

4. Test the implementation:
To test the lifting wavelet transform, you can create a sample input data and apply the transform using the `lifting_wavelet_transform` function.

```python
# Create sample input data
input_data = np.array([1, 2, 3, 4, 5, 6, 7, 8])

# Specify the number of lifting steps
num_steps = 3

# Apply the lifting wavelet transform
output_data = lifting_wavelet_transform(input_data, num_steps)

print(output_data)
```

5. Customize the lifting wavelet transform:
You can modify the lifting steps, use different wavelet filters, or implement additional operations according to your specific requirements.

Please note that this is a basic implementation of the lifting wavelet transform. It assumes a 1D input data and a specific number of lifting steps. You can extend and customize the code as needed for your specific application.

I hope this provides you with a starting point for implementing the lifting wavelet transform in Python.