![]() Now let’s apply this filter to an actual image and let’s see what we got. The ideal low pass filter can be graphically represented as An ideal low pass filter in frequency domain is given below. ![]() When 0 is placed inside, we get edges, which gives us a sketched image. This is a common example of high pass filter. Now as we increase the size of 1, blurring would be increased and the edge content would be reduced. When one is placed inside and the zero is placed outside, we got a blurred image. This is the common example of low pass filter. Ideal low pass and Ideal High pass filters The high pass frequency components denotes edges whereas the low pass frequency components denotes smooth regions. High pass frequency components and Low pass frequency components ![]() Derivative masks are also called as high pass filter.Blurring masks are also called as low pass filter.The relationship between blurring mask and derivative mask with a high pass filter and low pass filter can be defined simply as. Relationship between blurring mask and derivative mask with high pass filters and low pass filters. As the size of the mask grows, more edge content is increased.The edge content is increased by a derivative mask.The sum of all the values in a derivative mask is equal to zero.A derivative mask have positive and as well as negative values.As the size of the mask grow, more smoothing effect will take placeĪ derivative mask has the following properties.The edge content is reduced by using a blurring mask.The sum of all the values is equal to 1.All the values in blurring masks are positive.Blurring masksĪ blurring mask has the following properties. We are going to perform a comparison between blurring masks and derivative masks. The concept of mask has been discussed in our tutorial of convolution and masks. Before discussing about let’s talk about masks first. In this tutorial we will thoroughly discuss about them. In the last tutorial, we briefly discuss about filters.
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