Transforms in image processing, Exams of Digital Image Processing

spatial and frequency Domain and details on types of transforms

Typology: Exams

2018/2019

Uploaded on 01/07/2019

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Spatial domain and frequency domain
In spatial domain, we deal with images as it is. The value of the pixels of the image change with
respect to scene. Whereas in frequency domain, we deal with the rate at which the pixel values
are changing in spatial domain.
Spatial domain
In spatial domain, we directly deal with the image matrix.
Frequency Domain
We first transform the image to its frequency distribution. Then our black box system perform
whatever processing it has to performed, and the output of the black box in this case is not an
image, but a transformation. After performing inverse transformation, it is converted into an
image which is then viewed in spatial domain.
It can be pictorially viewed as
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Spatial domain and frequency domain In spatial domain, we deal with images as it is. The value of the pixels of the image change with respect to scene. Whereas in frequency domain, we deal with the rate at which the pixel values are changing in spatial domain. Spatial domain

In spatial domain, we directly deal with the image matrix. Frequency Domain We first transform the image to its frequency distribution. Then our black box system perform whatever processing it has to performed, and the output of the black box in this case is not an image, but a transformation. After performing inverse transformation, it is converted into an image which is then viewed in spatial domain. It can be pictorially viewed as

Convolution is a mathematical operation on two functions (f and g) to produce a third function that expresses how the shape of one is modified by the other. The term convolution refers to both the result function and to the process of computing it.

Transformation A signal can be converted from time domain into frequency domain using mathematical operators called transforms. There are many kind of transformation that does this. Some of them are given below.

  • Fourier Series
  • (^) Fourier transformation
  • Laplace transform
  • Z transform Frequency components Any image in spatial domain can be represented in a frequency domain. Frequency components divided is into two major components. High frequency components High frequency components correspond to edges in an image. Low frequency components Low frequency components in an image correspond to smooth regions.