# Refocusing light field images via Fourier Slice Photograph theorem

I am trying to refocus images from a microlens array light field using Ren Ng's Fourier Slice photograph theorem found in his thesis chapter 5, equation 5.7, which is available at https://stanford.edu/class/ee367/reading/Ren%20Ng-thesis%20Lytro.pdf

Fourier Slice Photograph theorem

$$P_a=F^{-2}\circ B_a \circ F^4$$

Where P is the refocused photograph, F is either inverse or forward Fourier transform, and B is the Fourier photographic imaging operator, which he gives as $$B_a [G](k_x,k_y)= \frac{1}{F} G(\alpha k_x, \alpha k_y, (1-\alpha)k_x, (1-\alpha)k_y)$$

which is given in equation 5.6. In words, the Fourier Slice photograph theorem means a photograph is the inverse 2D Fourier transform of a dilated 2D slice in the 4D Fourier transform of the light field.

My confusion is partly notation and applying it in python to an acquired light field image. To start, I understand what the Fourier transform is but when Ng says 4D Fourier transform of the light field, I am not sure how to interpret that. Currently, I have my light field as a NumPy array such that my u=i, v=j values index to the different sub-aperture images where each pixel in the sub-aperture image has an x and y coordinate, hence giving me a 4D light field array.

        lf_img = cv2.imread(lf_img, -cv2.IMREAD_ANYDEPTH)
row_lens = lf_img.shape/14 #14 is pixels under a microlens in this direction
col_lens = lf_img.shape/14 # 14 is pixels under a microlens in this direction
vp_imgs = np.zeros([14,14,int(row_lens),int(col_lens),3])
for i in range(14):
for j in range(14):
vp_imgs[i, j, :, :, :] = lf_img[i::14, j::14, 0:3] ##U,V,X,Y,(RGB)


Then I see images like this http://graphics.stanford.edu/papers/fourierphoto/ in Ng's thesis where to me it looks like he just took the 2D Fourier transform of each lenslet and arranged them in a grid. So I am not entirely sure how to interpret this 4D Fourier transform and how the slicing is done. Thanks!

Sample of what sort of light field image I am working with: The trick is avoiding the computation of $$4D$$ Fourier transform (high computational complexity) by using Fourier slice theorem. Its implementation is pretty tricky AFAIK. He never computes $$4D$$ transform. $$4D$$ transform is not much different from $$1D$$ to $$2D$$.