Mahotas – Soft Threshold

In this article we will see how we can implement soft threshold in mahotas. The soft thresholding is also called wavelet shrinkage, as values for both positive and negative coefficients are being “shrinked” towards zero, in contrary to hard thresholding which either keeps or removes values of coefficients.

In this tutorial we will use “luispedro” image, below is the command to load it. 

mahotas.demos.load('luispedro')

Below is the luispedro image 

In order to do this we will use mahotas.rc method  

Syntax : mahotas.thresholding.soft_threshold(image, t_value)
Argument : It takes image object and unit8 value as argument
Return : It returns image object 

Note : Input image should be filtered or should be loaded as grey

In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this 

image = image[:, :, 0]

Example 1:

Python3

# importing required libraries
import mahotas
import mahotas.demos
import numpy as np
from pylab import imshow, gray, show
from os import path

# loading the image
photo = mahotas.demos.load('luispedro')


# loading image as grey
photo = mahotas.demos.load('luispedro', as_grey = True)

# converting image type to unit8
# because as_grey returns floating values
photo = photo.astype(np.uint8)

# showing original image
print("Image")
imshow(photo)
show()

# unit 8 value
t = np.uint8(150)

# soft threshold
photo = mahotas.thresholding.soft_threshold(photo, t)


print("Image with soft threshold")

# showing image
imshow(photo)
show()

Output :

Example 2:

Python3

# importing required libraries
import mahotas
import numpy as np
from pylab import imshow, show
import os


# loading image
img = mahotas.imread('dog_image.png')


# setting filter to the image
img = img[:, :, 0]

print("Image")

# showing the image
imshow(img)
show()

# unit 8 value
t = np.uint8(180)

# soft threshold
img = mahotas.thresholding.soft_threshold(img, t)


print("Image with soft threshold")

# showing image
imshow(img)
show()

Output :