In the previous blog, we discussed filters and convolution operation. Before moving forward, let’s discuss an important concept “Frequency”, which is widely used in spatial filtering.

Frequency in images is the rate of change of intensity values. Thus, a high-frequency image is the one where the intensity values change quickly from one pixel to the next. On the other hand, a low-frequency image may be one that is relatively uniform in brightness or where intensity changes very slowly. Most images contain both high-frequency and low-frequency components. Let’s see by an example below

Clearly, in the above image, the zebra pattern has a high frequency as the intensity changes very rapidly from white to black. While the intensity changes very gradually in the sky thus it has low frequency.

It’s not hard to conclude that edges in an image represents high frequency because the intensity changes drastically across an edge.

Based on the frequency, we can classify the filters as

• Low Pass Filters
• High Pass Filters

Low Pass filters block high-frequency parts of an image and thus results in blurring or image smoothing. This is shown below

On the other hand, a high pass filter enhances high-frequency parts of an image (i.e. edges) and thus results in image sharpening.

In the next blog, we will discuss in detail different low pass and high pass filters, how to construct them and enhance an image. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.