def get_summary():
    text = '''
    This Daisi uses spline-filtering to compute an edge-image (the second derivative of a smoothed spline)
of a raccoon’s face, which is an array returned by the command scipy.misc.face.
The command sepfir2d was used to apply a separable 2-D FIR filter with mirror-symmetric boundary conditions
to the spline coefficients.
This function is ideally-suited for reconstructing samples from spline coefficients
and is faster than convolve2d, which convolves arbitrary 2-D filters and allows for
choosing mirror-symmetric boundary conditions.

### Step 1 : Load an image and convert to grayscale

```python
import pydaisi as pyd
from scipy import misc
import scipy
import numpy as np
import matplotlib.pyplot as plt


face = scipy.misc.face()
plt.gray()
plt.imshow(face)
plt.show()

image = misc.face(gray=True).astype(np.float32)
```


### Step 2 : call the "Compute B-spline" daisi from your code

```python
b_spline = pyd.Daisi("Compute B-Spline")
deriv = b_spline.compute_deriv(image=image).value
```

### Step 3 : Visualize the result

```python
import matplotlib.pyplot as plt
plt.imshow(deriv)
plt.show()
```
'''

    return text