![]() ![]() The final image has more than 90,000 individual sinusoidal gratings added together. The video shown above is sped up, and not all the frames are displayed. At the end of the video, the result is an image that’s identical to the original one. As the video goes on, more and more detail is added to the image. However, soon you’ll start to see the main shapes from the original image emerge. Early on in the video, the image on the right is not recognisable. Therefore, each sinusoidal grating you see on the left is added to all the ones shown previously in the video, and the result at any time is the image on the right. The image on the right shows the sum of all the sinusoidal gratings.The image on the left shows the individual sinusoidal gratings.In the video above and all other similar videos in this article: Vidoes showing sinusoidal gratings and image reconstruction Those with a keen interest in new Python projects, especially ones using NumPy Those who are keen on optics and the science of imagingĪnyone who’s interested in image processing Using The 2D Fourier Transform in Python to Reconstruct The ImageĪnyone wanting to explore using images in PythonĪnyone who wants to understand 2D Fourier transforms and using FFT in Python.Finding All The Pairs of Points in The 2D Fourier Transform.Reverse Engineering The Fourier Transform Data.Calculating the 2D Fourier Transform of An Image in Python.Creating Sinusoidal Gratings using NumPy in Python.Introduction: Every Image is Made Up of Only Sine Functions.But if you’d like to jump across the sections, then here’s an outline of the article: The best way to read this article is from top to bottom. ![]() This will not be a detailed, technical tutorial about the Fourier transform, although if you’re here to learn about Fourier transforms and Fourier synthesis, then you’ll find this post useful to read alongside more technical texts. I’ll describe the bits you need to know along the way. However, you don’t need to be familiar with this fascinating mathematical theory. I’ll guide you through the code you can write to achieve this using the 2D Fourier transform in Python In this article, I’ll convince you that any two-dimensional (2D) image can be reconstructed using only sine functions and nothing else. Another surprising one is sine functions with different parameters. What are the individual units that make up an image? Sure, one answer is pixels, each having a certain value.
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