Concept of Blurring
In Digital image processing, blurring is used to make an image smooth in which edges are not observed. Suppose, we have an image in which there are 508*340=172,720 pixels. If we want to blur this image, then all the 172,720-pixel values will be changed by using the blurring technique and convolution as we know that in an image, there are 8 pixels around it. When these pixels are combined as 8+1=9, a 3x3 matrix is formed. In our image, there are 172,720 pixels, so there will be 172,720 matrices of 3x3 orders. By applying convolution in all 172,720 matrices with a common matrix called the kernel. The kernel is a special matrix, it changes the pixels using convolution to make an image blur. A low pass filter is used for blurring as it allows the low frequency to allow and stop the high frequency. The term frequency means to change the value of the pixel.
Blurring vs. Zooming
When an image is blurred, and it is zoomed, the zooming factor is increased. This is because when we zoom an image, many new pixels are added and when an image is blurred the pixels of the normal image and blurred image remains the same.