This design file requires the following events file to be present in the same directory.Any modification undertaken here would be lost if the UI form were to be reopened for editing. This file is a read-only file the user is not supposed to modify this file.It also declares the callback event functions for each of the UI controls however, the actual definition of the event code appears in the next file. It defines the rules/code for the display of the figure (and any other additional UI controls that we add on to the form).This file contains the Scilab code for generating a UI form (also called a figure in the Scilab language).You would notice that there are three files (in addition to the calculate_fft.sce created in the first Step) which were generated by the designer: Navigate to the output folder, in my case it is Desktop/Example1. Ĭolumn 3 of the following pictures show the Airy pattern results of the circular aperture.Before we continue with the design of the UI, it is informative to look at the files generated by the Scilab GUI Designer in the last step. When light from a point source passes through a small circular aperture, it does not produce a bright dot as an image, but rather a diffuse circular disc known as Airy’s discs surrounded by much fainter concentric circular rings. This implementation presents a model of a circular aperture diffraction.The expected results shall be an Airy pattern. These are results of implementing FFT to circle of black background for different radii. Also using fft2() twice is just equivalent to implementing inverse fourier transform. It must be noted that fftshift() functions to interchange the quadrants of the image with respect to the original. The third column shows the result after implementing fftshift() and the last column shows the result after implementing double fft2(). The next column shows the result of implementing fft2() without fftshift(). For the proceeding images, the column 1 shows the raw image of the circle in black background. This part shows the FFT of a white circle image in black background. It must be noted that this blog report aims for the familiarization and implementation of FFT to aid in some future image processing techniques. The main difference one should take note between fft() and fft2() is that the former is used for one-dimensional signals whereas the latter is used for 2D signals or our images. These functions follows the Fast Fourier Transform algorithm by Cooley and Turkey and so its exertion for signal/image processing is quite fast and efficient. In Scilab, the implementation of FT to an image can be done simply with the help of the functions fft()and fft2(). This image may encode the following information of the signal (the spatial frequency, the magnitude (positive or negative), and the phase). The concept of Fourier Transform (FT), in image processing, states that any signal can be expressed as a sum of series of sinusoids. In the case of imagery, these are sinusoidal variations in brightness across the image. Our course is about image processing and so it is just mandatory that we should focus on FT’s significance to visual images!
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