[Week 2] Signals and Systems

Things are getting more exciting!

The week is divided to three segments. The first segment introduced some 2D and 3D discrete signals such as Images and videos, mentioning the meaning of Unit Impulse and Unit Step discrete signals, also the segment shows the separation availability between multiple numbers of signals.

In the second segment. It introduced the 2-dimenational complex exponential signal and how to build blocks of signals that have the same frequencies.

In the third segment. It was about 2D convolution examples in which some filters are used to manipulate the original image to produce an output image.

2D and 3D Discrete Signals

  • 2D discrete signals are signals that depend on 2 variables, each one of them has its own minimum and maximum values.
  • Example: Images
  • 2D Image consists of pixels, each pixel has its x-y coordinate in the image
  • The pixel holds 3 combined values which represent the RED, GREEN and BLUE values
  • If the image is Gray-scaled, this means that RED == GREEN == BLUE
  • 3D discrete signals have an additional variable
  • Videos could be considered 3D discrete signals because the dimensions are: x, y, z, where is z is the frame number in the video, and x, y are the dimension of the frame


Discrete Unit Impulse

  • Impulse signal is a function which value is zero everywhere except at zero
  • So, the discrete unit impulse of 2 signals is 1 if the values of the first and second signals are zero
  • The discrete unit impulse of 2 signals n1, n2 is eq
  • The signals are called “Separable” if eq

Discrete Unit Step

  • The discrete unit step of 2 signals is 1 if the values of the first and second signals are greater or equal zero
  • The discrete unit step of 2 signals, n1, n2 is eq

Complex Exponential Signals

  • The complex exponential of 2 signals are defined as eq
  • Where eq are the periodicity of eqsignals respectively
  • According to Euler formula, eq
  • Where eq is a rational multiple of eq

2D Systems

  • Systems that takes eq as an input, and using some operations or filters T[input], it produces eq
  • There are 3 kinds of systems
  • Systems are : Linear Systems, Spatially Invariant Systems and Linear and Spatially Invariant Systems

Linear System

  • Given the input eq where eq  is a weight
  • The system is called “Linear System” if the output of the input is eq

Spatially Invariant Systems

  • Given eq
  • The system is called “Spatially Invariant” if eq

Linear and Spatially Invariant Systems

  • Depends on the impulse response of the sight signal, the sight signal could be obtained from devices such as Camera, Telescope
  • Let eq be the impulse response of sight signal eq
  • Now, for any given input eq to the impulse response, the output is eq , where ** is 2D discrete convolution


Examples for 2D convolution

  • Noise Reduction
  • Edge Detection
  • Sharpen
  • Blur



[Week 1] Introduction to Image and Video Processing

An Introduction to the course, the week focuses on some definitions and basics about different kinds of signals that we deal with on our life.

Def Signal: In Electrical Engineering, a signal is a function that contains some information about an event that occurs in discrete/continuous time.


    Heart Signals               Human Voice

Analog vs. Digital Signals

  • To make any processing to a signal using any device (Computer, Mobile,.. etc.), we need to convert the signal that comes from the source which is in an Analog form to a Digital form.
  • To convert the signal to Digital form, the device transforms the acoustic signal to electrical signal
  • Def Acoustic Signal: the noise(voice, sound) that humans/animals produce
  • Def Electrical Signal: Signal that is generated by the microphone by converting the sound signal to voltage signal
  • The electrical signal transforms to digital signal using sampling and quantization
  • Def Sampling: Transform the continuous signal to discrete signal by take a sample from the input
  • Def Quantization: Get the amplitude of the discrete signal by mapping the large set of values to small values to be counted
  • After making the processing, we reverse the pervious steps to convert the digital signal to analog to be hearable


Images Classification

  • Reflection Images: Images information resource in the object’s surface, example {Radar}
  • Emission Images: Images information resource internally in the object, example {Infrared}
  • Absorption Images: Images information resource in the internal structure of the object, example