Smooth the edges of binary images (Face) using Python and Open CV

Question

I am looking for a perfect way to smooth edges of binary images. The problem is the binary image appears to be a staircase like borders which is very unpleasing for my further masking process.

I am attaching a raw binary image that is to be converted into smooth edges and I am also providing the expected outcome. I am also looking for a solution that would work even if we increase the dimensions of the image.

Problem Image Expected Outcome

Answer 1

To preserve the sharpness of a binary image, I would recommend applying something like a median filter. Here is an example of this:

from PIL import Image, ImageFilter


image = Image.open('input_image.png')
image = image.filter(ImageFilter.ModeFilter(size=13))
image.save('output_image.png')

which gives us the following results:

Figure 1. Left: The original input image. Right: The output image with a median filter of size 13.

Increasing the size of the filter would increase the degree of smoothing, but of course this comes as a trade-off because you also lose high-frequency information such as the sharp corner on the bottom-left of this sample image. Unfortunately, high-frequency features are similar in nature to the staircase-like borders.

Answer 2

You can do that in Python/OpenCV with the help of Skimage by blurring the binary image. Then apply a one-sided clip.

Input:

import cv2
import numpy as np
import skimage.exposure

# load image
img = cv2.imread('bw_image.png')

# blur threshold image
blur = cv2.GaussianBlur(img, (0,0), sigmaX=3, sigmaY=3, borderType = cv2.BORDER_DEFAULT)

# stretch so that 255 -> 255 and 127.5 -> 0
# C = A*X+B
# 255 = A*255+B
# 0 = A*127.5+B
# Thus A=2 and B=-127.5
#aa = a*2.0-255.0 does not work correctly, so use skimage
result = skimage.exposure.rescale_intensity(blur, in_range=(127.5,255), out_range=(0,255))

# save output
cv2.imwrite('bw_image_antialiased.png', result)

# Display various images to see the steps
cv2.imshow('result', result)

cv2.waitKey(0)
cv2.destroyAllWindows()

You will have to adjust the amount of blur for the degree of aliasing in the image.