Python Pillow - Correcting Color Balance

---

---

Correcting color balance, in the context of image processing, refers to the adjustment of the intensities of different colors within an image to achieve a desired visual outcome. This adjustment typically involves the primary colors of red, green, and blue and is aimed at rendering specific colors, especially neutral colors like white or gray, correctly. Correct color balance ensures that the colors in an image appear as they would in the real world, making it look more naturalistic and pleasing to the human eye.

The Python pillow library provides several options to correct the color balance of an image. Let's explore them below.

Correcting Color Balance of an Image

Correcting or adjusting an image's color balance can simply be done by using the PIL.ImageEnhance.Color() class. This class provides the ability to fine-tune the color balance of an image by changing the enhancement factor.

Below is the syntax of the ImageEnhance.Color() class −

class PIL.ImageEnhance.Color(image)

An enhancement factor is a floating-point value passed to the common single interface method, enhance(factor), which plays an important role in adjusting image color balance. When a factor of 0.0 is used, it will produce a solid grey image. A factor of 1.0 will give the original image.

Following are the steps to adjust the color balance of an image −

• Create a color enhancer object using the ImageEnhance.Color() class.
• Then apply the enhancement factor using the enhancer_object.enhance() method.

Example

Here is an example that uses the PIL.ImageEnhance.Color class to adjust the color balance of an image.

from PIL import Image, ImageEnhance

# Open the image
input_image = Image.open("Images/Tajmahal_2.jpg")

# Create a ColorEnhance object
color_enhancer = ImageEnhance.Color(input_image)

# Adjust the color balance by setting the enhancement factor
enhancement_factor = 1.5
color_balanced_image = color_enhancer.enhance(enhancement_factor)

# Display the original and color balanced images 
input_image.show()
color_balanced_image.show()

Input Image

Output Color Balanced Image −

Correcting Color Balance using the Point Transforms

Here is another approach of applying point transforms to color channels of an image for adjusting the color balance of an image. Point transforms can generally be used to adjust various aspects of an image, including color balance, brightness, and contrast. This can be done by using the point() method, it helps to Process the individual bands/ channels of an image.

Example

Here is an example that demonstrates the application of point transforms to correct the color balance of an image.

from PIL import Image

# Open the image
input_image = Image.open("Images/tree.jpg")

# Extract the red (R), green (G), and blue (B) channels
red_channel, green_channel, blue_channel = input_image.split()

# Adjust the color balance by scaling the intensity of each channel
# Increase green intensity
green_channel = green_channel.point(lambda i: i * 1.2)

# Decrease blue intensity
blue_channel = blue_channel.point(lambda i: i * 0.8)  

# Merge the channels back into an RGB image
corrected_image = Image.merge("RGB", (red_channel, green_channel, blue_channel))

# Display the input and corrected images
input_image.show()
corrected_image.show()

Input Image

Output Image

Output Color Balanced Image −

Example

The following example adjusts the color balance of an image by applying a transformation matrix to its color channels.

from PIL import Image

# Open the input image
input_image = Image.open("Images/Tajmahal_2.jpg")

# Define a transformation matrix to adjust color channels
# Multiply R (red) by 1.2, leaving G (green) and B (blue) unchanged
transformation_matrix = (1.5, 0, 0, 0,
   0, 1, 0, 0,
   0, 0, 1, 0)

# Apply the color channel transformation and create the corrected image
corrected_image = input_image.convert("RGB", transformation_matrix)

# Show the original and corrected images
input_image.show()
corrected_image.show()

Input Image

Output Image

Output Color Balanced Image −