All of the examples can be found in Jupyter notebook form here.

# Tomography

Tomography is the process of reconstructing a density distribution from given integrals over sections of the distribution. In our example, we will work with tomography on black and white images. Suppose $x$ be the vector of $n$ pixel densities, with $x_j$ denoting how white pixel $j$ is. Let $y$ be the vector of $m$ line integrals over the image, with $y_i$ denoting the integral for line $i$. We can define a matrix $A$ to describe the geometry of the lines. Entry $A_{ij}$ describes how much of pixel $j$ is intersected by line $i$. Assuming our measurements of the line integrals are perfect, we have the relationship that

\[ y = Ax\]

However, anytime we have measurements, there are usually small errors that occur. Therefore it makes sense to try to minimize

\[ \|y - Ax\|_2^2.\]

This is simply an unconstrained least squares problem; something we can readily solve!

```
using Convex, ECOS, DelimitedFiles, SparseArrays
aux(str) = joinpath(@__DIR__, "aux_files", str) # path to auxiliary files
line_mat_x = readdlm(aux("tux_sparse_x.txt"))
summary(line_mat_x)
line_mat_y = readdlm(aux("tux_sparse_y.txt"))
summary(line_mat_y)
line_mat_val = readdlm(aux("tux_sparse_val.txt"))
summary(line_mat_val)
line_vals = readdlm(aux("tux_sparse_lines.txt"))
summary(line_vals)
```

"3300×1 Array{Float64,2}"

Form the sparse matrix from the data Image is 50 x 50

`img_size = 50`

50

The number of pixels in the image

```
num_pixels = img_size * img_size
line_mat = spzeros(length(line_vals), num_pixels)
num_vals = length(line_mat_val)
for i in 1:num_vals
x = Int(line_mat_x[i])
y = Int(line_mat_y[i])
line_mat[x + 1, y + 1] = line_mat_val[i]
end
pixel_colors = Variable(num_pixels)
# line_mat * pixel_colors should be close to the line_integral_values
# to reflect that, we minimize a norm
objective = sumsquares(line_mat * pixel_colors - line_vals)
problem = minimize(objective)
solve!(problem, () -> ECOS.Optimizer(verbose=0))
rows = zeros(img_size*img_size)
cols = zeros(img_size*img_size)
for i = 1:img_size
for j = 1:img_size
rows[(i-1)*img_size + j] = i
cols[(i-1)*img_size + j] = img_size + 1 - j
end
end
```

Plot the image using the pixel values obtained!

```
using Plots
image = reshape(evaluate(pixel_colors), img_size, img_size)
heatmap(image, yflip=true, aspect_ratio=1, colorbar=nothing, color=:grays)
```

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