Digital Image Processing Basics
Introduction
Digital image processing is the acquisition, analysis, and performing of a wide range of operations on an image.
In digital image processing, there are three types of images:
- Binary
- Grayscale
- RGB The digital image can be represented within the two-dimensional array.
A binary image is represented by a dimensional array where each cell stores 0 (black) and 1 (white) values.
A grayscale image is a two-dimensional array where values in the array lay in a range from 0 to 255
The RGB has the same range of values as grayscale but has 3 two-dimensional arrays representing each color — Red, Green, and Blue.
With this knowledge, we can, therefore, calculate how many bits are required to store an image: For binary images, width * height, since each pixel requires 1 bit, we can omit it. For grayscale image: width * height * 8, since each pixel is represented by 8 bits For RGB image: width * height * 8 * 3, because it has 3 planes, each plane contains values in a range from 0 to 255
Let’s start with image acquisition. In this article, I will be using Python and the OpenCV library.
We can use the method imread from the OpenCV library for image acquisition.
import cv2 as cv
img = cv.imread('gray.jpg')
cv.imshow('Grayscale image', img)
cv.waitKey()
cv.destroyAllWindows()Now, you should see the image.
It’s time for more exciting things. We can do the following mathematical operations on an image:
- Addition — blend two images together
- Subtraction — find the absolute differences between two images
- Multiplication — adding color to line drawing
- Division — find the relative differences between two images.
Addition
mport cv2 as cv
img_one = cv.imread('img/image1.png')
img_two = cv.imread('img/image2.png')
img_sum = img_one + img_two
cv.imshow('Sum', img_sum)
cv.waitKey()
cv.destroyAllWindows()