Image Enhancement

Performing mathematical operations can be used to perform image processing.

Addition and Subtraction

  • Adddition of each pixel of image with some number results in the image being brigther.
matrix = np.ones(img.shape, dtype="uint8") * 50

img_brighter = cv2.add(img, matrix)
  • Subtraction of each pixel of image with some number results in the image being darker.
matrix = np.ones(img.shape, dtype="unit8") * 50

img_darker = cv2.subtract(img, matrix)

Multiplication (Contrast)

  • multiplication can be used to improve the contrast of the image.
  • Contrast : difference in the intensity values of the pixels of an image.
  • Multiplying the intensity values with a constant can make the difference larger or smaller ( if multiplying factor is < 1 ).
matrix1 = np.ones(img.shape) * 0.8
matrix2 = np.ones(img.shape) * 1.2

img_darker   = np.uint8(cv2.multiply(np.float64(img), matrix1))
img_brighter = np.uint8(cv2.multiply(np.float64(img), matrix2))
  • After multiplying, the values which are already high, become greater than 255
    • This cause and overflow issue.
  • Therefore, use np.clip
    • It will clip the pixel values between 0 and 255. 
      img_higher = np.uint8(np.clip(cv2.multiply(np.float64(img), matrix2), 0, 255))

Image Threshold

  • Image Masks : allows to process on specific parts of an image keeping the other parts intact.
  • Image Thresholding is used to create Binary Images from grayscale images.
  • Binary Images are used to create masks.

Global Threshold

retval, dst = cv2.threshold( src, thresh, maxval, type[, dst] )
  • src : input array
  • dst : output array
  • thresh : threshold value
  • maxval : maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.
  • type : thresholding type

AdaptiveThreshold

dst = cv.adaptiveThreshold( src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst] )

  • maxValue : Non-zero value assigned to the pixels for which the condition is satisfied

  • adaptiveMethod : Adaptive thresholding algorithm to use.

  • blockSize : Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.

  • C: Constant subtracted from the mean or weighted mean (see the details below). Normally, it is positive but may be zero or negative as well.

Bitwise Operations

  • Given 2 images img1 and img2
dst = cv2.bitwise_and( src1, src2[, dst[, mask]] )
  • Operations available :
    • cv2.bitwise_and()
    • cv2.bitwise_or()
    • cv2.bitwise_xor()
    • cv2.bitwise_not()

Collab Notebook here