Opencv | Spatial Filters

Hello, everyone. Today, we are talking about spatial filters, a popular way to take object enhancement for special situations. For instance, there are lots of pictures of the moon in NASA, which is not so clear for us to analyse surface of the moon, so we are willing to make some enhancement in them. Let’s learn some sample theories.

Principal

The principal objective of enhancement is to process an image so that the result is more suitable than the original image for specific application.
Image enhancement approaches fall into two broad categories:
Spatial domain methods: directly on image pixels
Frequency-domain: on transform coefficients
There is no general theory of image enhancement.
The viewer is the ultimate judge of how well a particular method work, and visual evaluation is a highly subjective process.

Spatial domain enhancement processes will be denoted by the expression.
Enhancement usually involves a neighborhood about a point (x,y) which is a square or rectangular subimage centered at (x,y).
The simplest form of T is when the neighborhood is of size 1x1 (a single pixel).

Histogram Processing

The histogram of a digital image with gray-levels in the range [0, L-1] is a discrete function where h(k) is the kth gray level and r(k) is the number of pixels in the image having gray level .
It is common practice to normalize a histogram by dividing each of its values by the total number of pixels in the image, denoted by n.
The goal is to obtain a uniform histogram for the output image given any input image.

Obviously, four pictures with different histogram make a conclusion that if we make histogram more equal, it will have more clear appearance. That’s so useful for astronomy! Please look at these pictures!