The 2D Fourier Transform extends the principles of the 1D transform to two dimensions:
Here,
now
upon adding all these scale and shifted 2D sinosoids we get the final 2D image just like we did in 1D fourier transform.
In
Understanding the appearance and behavior of 2D sinusoids in the context of the Fourier Transform requires a closer look at the
Imagine a coded animation, similar to the one in the 1D Fourier transform blog, to better visualize this concept. As you increment the value of
Each 2D sinusoid is shaped by a corresponding phasor. This phasor adjusts both the amplitude and phase of the sinusoid. The resultant effect is a versatile range of sinusoidal waves, each uniquely contributing to the overall image reconstruction process in MRI.
The number of wiggles or oscillations in each direction correlates directly to a specific point in the
Here's a part of animation that demonstrates the relation between point in
If you keep on increasing the cycles in either direction
- It has to do with the number of point i am sampling to create the 2D sinosoid and nyquist frequency:D
As a proof of concept, let's consider a Mario sprite image. We start by taking its Fourier transform, represented as:
Here,
To reconstruct the original image from its Fourier transform, we take each point in the
-
Constructing 2D Sinusoids: For each point
$(k_x, k_y)$ in the Fourier transform, we create a corresponding 2D sinusoid in its complex exponential form:$$S(x, y; k_x, k_y) = e^{2\pi i(k_x x + k_y y)}$$ -
Scaling with the Phasor: The phasor at each point in the
$F(k_x, k_y)$ plane, characterized by an amplitude$A$ and phase$\phi$ , scales the corresponding 2D sinusoid. This scaling is represented as:$$S_{scaled}(x, y; k_x, k_y) = A \cdot e^{2\pi i(k_x x + k_y y) + \phi}$$ -
Summing Spatial Frequencies: The final step involves summing these scaled and shifted sinusoids across all spatial frequencies to reconstruct the original image:
$$f_{reconstructed}(x, y) = \sum_{k_x, k_y} S_{scaled}(x, y; k_x, k_y)$$
This method demonstrates the practical application of Fourier Transform in image processing, showcasing how an image can be decomposed and then reconstructed using the principles of spatial frequencies and phase shifts.
Fig 3: 1: oringal image, 2: 2D FT of the image, 3: Indexing phasors, 4: 2D sine wave associated with each phasor. Addtion of the 2D SinosoidsIn Fig.3 as we keep adding the 2D sinosoids we get the final image. This is the essence of the Fourier Transform: decomposing a signal into its 2D sine patterns and then reconstructing it from these components.