Contrast Stretching Using Python And Pillow

Overview:

  • Many of the images acquired, be it photographs or medical images from modalities like CT, MR, NM often require certain image enhancements for viewing the details hidden in the images through normal eyes.
  • Contrast stretching or Normalization is one such operation applied on images, which improves the contrast of the image so that the details present in the image can be clearly seen.

 

Point operation on spatial domain:

  • Contrast stretching is applied directly on the image by modifying each pixel present in the image - that is by applying point operations rather then using a kernel.

  • By not using a kernel means each pixel value is not determined using the values of a neighborhood pixels. The intensity value of the pixel is modified using a normalization formula.

  • Contrast stretching is applied directly on the image, it does not involve converting an image to some intermediate form and finally applying an inverse transform to get the image back.

  • Contrast stretching of an image modifies the pixel values of the image in such a way that the intensities are transformed into a bigger range.

  • Contrast stretching is a linear operation which means the value of the new pixel linearly varies based on the value of original pixel.

  • A contrast-enhanced image can be converted back to the original image, as the transformation applied is linear.

  • Contrast stretching maps one intensity range present in the image to another intensity range.

  • The new intensity range should be selected using the histogram of an Image so that the minimum intensity and the maximum intensity values present in the image are carefully selected excluding the outliers.

 

The Formula for Contrast stretch or Image Normalization

 

Io = (Ii-Mini)*(((Maxo-Mino)/(Maxi-Mini))+Mino)

 

Io                                - Output pixel value

Ii                                 - Input pixel value

Mini                         - Minimum pixel value in the input image

Maxi                        - Maximum pixel value in the input image

Mino                        - Minimum pixel value in the output image

Maxo                       - Maximum pixel value in the output image

 

 

Contrast stretch using Python and Pillow:

  • The Python Image Processing Library supports point image operations through method point()of the Image module.
  • The point()method takes a function as a parameter. The function passed in accepts one argument and typically this is the pixel value that is to be transformed.
  • In case of contrast stretching of an image, the formula for contrast stretching can be implemented inside a function, which takes the pixel value as a parameter and returns the modified intensity of the pixel.

 

 

Example:

# Example Python Program for contrast stretching

from PIL import Image

 

# Method to process the red band of the image

def normalizeRed(intensity):

    iI      = intensity

   

    minI    = 86

    maxI    = 230

 

    minO    = 0

    maxO    = 255

 

    iO      = (iI-minI)*(((maxO-minO)/(maxI-minI))+minO)

    return iO

 

# Method to process the green band of the image

def normalizeGreen(intensity):

    iI      = intensity

   

    minI    = 90

    maxI    = 225

 

    minO    = 0

    maxO    = 255

 

    iO      = (iI-minI)*(((maxO-minO)/(maxI-minI))+minO)

    return iO

 

# Method to process the blue band of the image

def normalizeBlue(intensity):

    iI      = intensity

   

    minI    = 100

    maxI    = 210

 

    minO    = 0

    maxO    = 255

 

    iO      = (iI-minI)*(((maxO-minO)/(maxI-minI))+minO)

    return iO

 

 

# Create an image object

imageObject     = Image.open("./glare4.jpg")

 

# Split the red, green and blue bands from the Image

multiBands      = imageObject.split()

 

# Apply point operations that does contrast stretching on each color band

normalizedRedBand      = multiBands[0].point(normalizeRed)

normalizedGreenBand    = multiBands[1].point(normalizeGreen)

normalizedBlueBand     = multiBands[2].point(normalizeBlue)

 

# Create a new image from the contrast stretched red, green and blue brands

normalizedImage = Image.merge("RGB", (normalizedRedBand, normalizedGreenBand, normalizedBlueBand))

 

# Display the image before contrast stretching

imageObject.show()

 

# Display the image after contrast stretching

normalizedImage.show()

 

Input Image for Contrast Stretching Operation:

Before contrast stretching is applied onto the image

Courtesy: Miguel Virkkunen Carvalho

Histogram of the Input Image(Helps in defining the intensity range):

Red Band of the Image:

Histogram for the red band of the image

Green Band of the Image:

Histogram for the green band of the image

Blue Band of the Image:

Histogram for the blue band of the image

Output Image after applying contrast stretching:

After contrast stretching is applied onto the image


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