What is Fourier transform?


The term “Fourier transform” may be familiar to you from math or engineering lectures. It evokes a sense of awe and fascination. The Fourier transform is a mathematical technique widely used in the fields of signal processing, image analysis, and data compression. But what exactly is the Fourier transform? Why does it exist everywhere? Let’s unravel these mysteries and more.

Fourier Transform – The Foundation of Modern Data Analysis

1.1 What is the Fourier Transform?

Simply put, the Fourier transform is a mathematical method for converting signals between the time (or spatial) domain and the frequency domain. It is named after the French mathematician Jean-Baptiste Joseph Fourier. At first glance, it may seem like a dense concept, but as we delve deeper, we’ll uncover its fascinating nature.

A piece of music is made up of various notes coming together. Each note produces a different pitch and duration. This can be considered as representing music in the “time domain.”

On the other hand, music can also be represented in the “frequency domain,” which shows how much sound is contained at each pitch.

The Fourier transform allows us to move back and forth between these two domains. Imagine you need directions to go from your friend’s house (time domain) to a park (frequency domain). By traveling, you can go from your friend’s house to the park. This can be likened to “moving” from the time domain to the frequency domain.

Furthermore, if you decide to return to your friend’s house after playing in the park, what would you do? You would need directions to get back. If there are directions from the park to your friend’s house, you can return. This can be likened to “moving” from the frequency domain to the time domain. The means to facilitate this “movement,” i.e., the guidance to go back and forth between the time domain and the frequency domain, is called the “Fourier transform.” Therefore, the Fourier transform can be seen as a tool to convert information between the time domain (e.g., the music composition) and the frequency domain (the distribution of pitches in the composition).

The purpose of knowing the frequency domain information is to understand how much sound is present at each pitch (frequency domain) and to grasp which sounds occur in a composition. Once you understand this, you can determine the overall characteristics of the composition, such as whether it has a bright or dark atmosphere, or whether it feels lively or calm.

Moreover, professionals in music use this frequency information to mix songs or adjust the sound. For example, music producers adjust the volume of each sound (frequency) to make the vocals and instruments sound balanced. To do this, they need to know how much each sound (frequency) is present.

Thus, frequency information is an essential tool for creating and understanding music. That’s why the Fourier transform is applied.

1.2 Origins and Historical Significance

The origins of the Fourier transform can be traced back to the early 19th century.

At the time, Fourier was studying the phenomenon of heat conduction, where the warmth (heat) gradually spreads from one part of a substance to another. However, there was no accurate mathematical method to explain how heat spreads.

That’s when he came up with a new approach to understand heat conduction. He proposed representing the spread of heat as a combination of sine waves (i.e., oscillating waves). To express how the spread of heat changes over time, he suggested adding these waves together. This marked the beginning of “Fourier series.”

The Fourier series is the idea that any shape of waveform can be represented as a sum of simple sine waves. This overturned the prevailing understanding of the shape of “waves” at the time.

In addition, Fourier’s theory was mathematically challenging due to the representation of waveforms using an infinite sum of sine waves. As a result, it was difficult to fully understand the theory and evaluate whether it was truly correct. Initially, Fourier’s new theory faced significant opposition. However, truth prevails, and Fourier’s concept was eventually accepted. It became a milestone in the development of Fourier transform, a cornerstone in the chronicles of mathematics and physics.

1.3 Development of the Fourier Transform and its Application in the Real World

Fourier’s new idea allowed for the representation of things as a collection of simple waveforms, namely sine waves. This became known as the “Fourier series.”

However, his idea didn’t stop there. Fourier demonstrated that not only simple sine waves but also more complex waveforms could achieve the same result. In other words, he showed that any signal that varies with time can be represented as a collection of waveforms with different frequencies.

By using this approach, it became possible to convert signals from the time domain (i.e., signals that vary with time) to the frequency domain (i.e., how much of each frequency waveform the signal contains). This is the origin of the “Fourier transform.”

Conversely, it also became possible to convert from the frequency domain to the time domain. This is known as the “inverse Fourier transform.” In other words, the Fourier transform and inverse Fourier transform enable free movement between the time domain and the frequency domain.

These discoveries have made significant contributions to problem-solving in various fields, including engineering, physics, mathematics, image processing, and acoustics.

From image processing to audio compression, the Fourier transform is indispensable in various real-world applications. Its ability to break down complex data into more manageable and understandable chunks makes it invaluable in diverse fields.

Decoding the Mathematics of the Fourier Transform

2.1 Fourier Series vs. Fourier Transform

The Fourier series is a method for representing waveform-like functions as a combination of simple sine waves. On the other hand, the Fourier transform deals with non-periodic functions and provides an amplitude spectrum.

The “amplitude spectrum” represents the amplitude (i.e., magnitude or strength) of each frequency component that sounds or signals possess. Sounds or signals are composed of a combination of waves with several different frequencies. The amplitude spectrum indicates the strength of each frequency component.

For example, in the world of music, the amplitude spectrum determines the “color” or “timbre” of a sound. When the amplitude of high frequencies is high, the sound is perceived as “bright” or “sharp.” Conversely, when the amplitude of low frequencies is high, the sound is perceived as “dark” or “soft.”

These amplitude spectra can be calculated from signals in the time domain (e.g., audio recordings) using the Fourier transform. In other words, the Fourier transform can convert signals in the time domain to the frequency domain and represent the result as an amplitude spectrum.

2.2 Understanding the Mathematics of the Fourier Transform

The heart of the Fourier transform is the Fourier integral, which takes a function of time, f(t), and converts it into a function of frequency, F(f). Understanding it may be initially challenging, but it gets better with time, much like a good wine. Watch this informative video:

Applications of the Fourier Transform

3.1 Fourier Transform in Signal Processing

Signal processing is an area where the Fourier transform truly shines. It is widely used for signal analysis, filtering, and data compression. The fact that mathematics can make our music sound better is truly amazing, isn’t it?

3.2 Image Analysis and the Fourier Transform

The Fourier transform has revolutionized the field of image analysis, enabling us to filter images and perform operations such as blurring and edge detection.

The applications of Fourier transform in image analysis are incredibly diverse, but some of the main ones include image filtering, compression, and edge detection.

  1. Image filtering: Fourier transform is used to remove specific frequency components from an image. For example, removing high-frequency components (fine patterns or noise) can blur (smooth) the image, while removing low-frequency components (large shapes or structures) can enhance the edges of the image. These operations involve transforming the image to the frequency domain, adjusting specific frequency components, and then converting it back to the time (spatial) domain.
  2. Image compression: Fourier transform is also used as a technique to reduce the data size of an image. Image compression algorithms like JPEG use a variation of the Fourier transform called the Discrete Cosine Transform (DCT). By transforming the image to the frequency domain and removing high-frequency components that are not visually important, the data size can be reduced.
  3. Edge detection: Fourier transform can assist in detecting edges (boundaries) in an image. This is because edge regions contain a significant amount of high-frequency components. By emphasizing high-frequency components in the frequency domain and converting them back to the time (spatial) domain, the edge regions can be made more distinct.

As evident from these applications, Fourier transform is a crucial tool in image analysis.

3.3 Fourier Transform and Quantum Mechanics

Quantum mechanics is another field where Fourier transform plays a significant role. In quantum mechanics, Fourier transform is used to switch between different representations of quantum states.

Frequently Asked Questions (FAQ)

Q1: What is the fundamental purpose of Fourier transform?

The fundamental purpose of Fourier transform is to decompose a signal into its constituent frequencies. This allows us to analyze complex signals in the frequency domain, often providing insights and understanding.

Q2: Where is Fourier transform used?

Fourier transform is used in various applications, including signal processing, image analysis, audio and video compression, quantum physics, and machine learning.

Q3: Can Fourier transform handle non-periodic functions?

Yes, Fourier transform can handle non-periodic functions. Unlike Fourier series, which is for periodic functions, Fourier transform extends the concept to analyze non-periodic functions.

Q4: Who invented Fourier transform?

Fourier transform is named after the French mathematician Jean-Baptiste Joseph Fourier. His work on Fourier series laid the foundation for the development of Fourier transform.

Q5: Is it important for engineers to understand Fourier transform?

Yes, understanding Fourier transform is important, especially for engineers involved in signal processing, communications, and control systems.

Q6: What is the difference between Fourier series and Fourier transform?

Fourier series is a method to represent periodic functions or signals as a sum of simple sine waves, while Fourier transform is used to transform signals between the time (or spatial) domain and the frequency domain, enabling the analysis of non-periodic functions.


Exploring Fourier transform is like embarking on an exhilarating intellectual adventure, where complex concepts transform into captivating patterns of sine waves and frequencies. From Fourier’s groundbreaking work to its indispensable role in modern data analysis, Fourier transform stands as a testament to the beauty and power of mathematical thinking. Whether you’re a seasoned engineer, an aspiring scientist, or a curious reader, delving into the realm of Fourier transform promises rich knowledge and insightful rewards.