How to improve image quality? [closed]

Answer 1

One way to do it, is to convert your image to grayscale and then use a threshold value to compare with every pixel in order to decide whether it should be black or white. Pillow is a library that you can use for this type of processing:

from PIL import Image

black = (0,0,0)
white = (255,255,255)
threshold = (160,160,160)

# Open input image in grayscale mode and get its pixels.
img = Image.open("image.jpg").convert("LA")
pixels = img.getdata()

newPixels = []

# Compare each pixel 
for pixel in pixels:
    if pixel < threshold:
        newPixels.append(black)
    else:
        newPixels.append(white)

# Create and save new image.
newImg = Image.new("RGB",img.size)
newImg.putdata(newPixels)
newImg.save("newImage.jpg")

Result image:

Answer 2

The answer of @vasilisg. is a very nice sollution. One way to further improve upon this is to remove the remaining spots using a morphological opening operation. However, this will only work for spots smaller than the linethickness of the numbers in your image. Another option is to remove 'islands' of less than N pixels using the openCV connected components module. You could do this for example as following:

# External libraries used for
# Image IO
from PIL import Image

# Morphological filtering
from skimage.morphology import opening
from skimage.morphology import disk

# Data handling
import numpy as np

# Connected component filtering
import cv2

black = 0
white = 255
threshold = 160

# Open input image in grayscale mode and get its pixels.
img = Image.open("image.jpg").convert("LA")
pixels = np.array(img)[:,:,0]

# Remove pixels above threshold
pixels[pixels > threshold] = white
pixels[pixels < threshold] = black


# Morphological opening
blobSize = 1 # Select the maximum radius of the blobs you would like to remove
structureElement = disk(blobSize)  # you can define different shapes, here we take a disk shape
# We need to invert the image such that black is background and white foreground to perform the opening
pixels = np.invert(opening(np.invert(pixels), structureElement))


# Create and save new image.
newImg = Image.fromarray(pixels).convert('RGB')
newImg.save("newImage1.PNG")

# Find the connected components (black objects in your image)
# Because the function searches for white connected components on a black background, we need to invert the image
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(np.invert(pixels), connectivity=8)

# For every connected component in your image, you can obtain the number of pixels from the stats variable in the last
# column. We remove the first entry from sizes, because this is the entry of the background connected component
sizes = stats[1:,-1]
nb_components -= 1

# Define the minimum size (number of pixels) a component should consist of
minimum_size = 100

# Create a new image
newPixels = np.ones(pixels.shape)*255

# Iterate over all components in the image, only keep the components larger than minimum size
for i in range(1, nb_components):
    if sizes[i] > minimum_size:
        newPixels[output == i+1] = 0

# Create and save new image.
newImg = Image.fromarray(newPixels).convert('RGB')
newImg.save("newImage2.PNG")

In this example I have performed both the opening and connected component method, however if you use the connected component method you can usually omit the opening operation.

The results look like this:

After thresholding and opening:

After thresholding, opening and connected component filtering: