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[Tech Bites] Importance of Phase in Image Processing

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It is widely known that images are composed of pixels representing brightness. But what is less known is that images can also be viewed as a combinations of wavelengths of varying frequencies.

As seen in the video below, image processing based on a two-dimensional(2D) wave frequencies and their directionality is called Fourier transform.

Figure : 2D image synthesis (right) from frequency information (left) based on Fourier transform principles

Now that we know some key characteristics of images, we will explore the role and importance of phase and magnitude on accuracy of image processing.

Figure 2: Inverse Fourier transform  results of Fourier-transformed cameraman (top) and Lena (bottom) images following phase exchange

Assume we first Fourier transform the images of a cameraman and Lena (Figure 2). Then, if we exchange the phases of each respective image while maintaining their magnitudes, and inverse Fourier transform them, what will we see? The original image of the cameraman will display the Lena image and vice versa.

This illustration demonstrates that phase contains richer geometric information than magnitude. Accordingly, image processing in the frequency domain based on phase may be more efficient than that in the spatial domain based on pixels (e.g., convolutional filtering) and better adept to handle contrast changes resulting from different lightings.

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Research Lab

10F Kwangsung Bldg, 11, Yeoksam-ro 3-gil, Gangnam-gu, Seoul, Republic of Korea

Headquarter

7F Kwangsung Bldg, 11, Yeoksam-ro 3-gil, Gangnam-gu, Seoul, Republic of Korea

Email

Tel

Fax

admin@rtm.ai

02.2088.6780

070.7543.6780

© 2022 RTM. All Rights Reserved.
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