- DIP - Computer Vision and Graphics
- DIP - Optical Character Recognition
- DIP - JPEG compression
- DIP - Introduction to Color Spaces
- DIP - High Pass vs Low Pass Filters
- DIP - Convolution theorm
- DIP - Fourier series and Transform
- DIP - Frequency Domain Analysis
- DIP - Laplacian Operator
- DIP - Krisch Compass Mask
- DIP - Robinson Compass Mask
- DIP - Sobel operator
- DIP - Prewitt Operator
- DIP - Concept of Edge Detection
- DIP - Concept of Blurring
- DIP - Concept of Masks
- DIP - Concept of convolution
- DIP - Gray Level Transformations
- DIP - Histogram Equalization
- DIP - Introduction to Probability
- DIP - Histogram Stretching
- DIP - Histogram Sliding
- DIP - Image Transformations
- DIP - Brightness and Contrast
- DIP - Histograms Introduction
- DIP - Concept of Dithering
- DIP - ISO Preference curves
- DIP - Concept of Quantization
- DIP - Gray Level Resolution
- DIP - Pixels Dots and Lines per inch
- DIP - Spatial Resolution
- DIP - Zooming methods
- DIP - Concept of Zooming
- DIP - Pixel Resolution
- DIP - Concept of Sampling
- DIP - Grayscale to RGB Conversion
- DIP - Color Codes Conversion
- DIP - Types of Images
- DIP - Concept of Bits Per Pixel
- DIP - Perspective Transformation
- DIP - Concept of Pixel
- DIP - Camera Mechanism
- DIP - Image Formation on Camera
- DIP - Concept of Dimensions
- DIP - Applications and Usage
- DIP - History of Photography
- DIP - Signal and System Introduction
- DIP - Image Processing Introduction
- DIP - Home
DIP Useful Resources
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Introduction to Frequency domain
We have deal with images in many domains. Now we are processing signals (images) in frequency domain. Since this Fourier series and frequency domain is purely mathematics, so we will try to minimize that math’s part and focus more on its use in DIP.
Frequency domain analysis
Till now, all the domains in which we have analyzed a signal , we analyze it with respect to time. But in frequency domain we don’t analyze signal with respect to time, but with respect of frequency.
Difference between 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.
For simppcity, Let’s put it this way.
Spatial domain
![Frequency Domain](/dip/images/frequencydomain1.jpg)
In simple spatial domain, we directly deal with the image matrix. Whereas in frequency domain, we deal an image pke this.
Frequency Domain
We first transform the image to its frequency distribution. Then our black box system perform what ever 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
![Frequency Domain](/dip/images/frequencydomain2.jpg)
Here we have used the word transformation. What does it actually mean?
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
Out of all these, we will thoroughly discuss Fourier series and Fourier transformation in our next tutorial.
Frequency components
Any image in spatial domain can be represented in a frequency domain. But what do this frequencies actually mean.
We will spanide frequency components 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.
Advertisements