2D Convolution as a Doubly Block Circulant Matrix Operating on a Vector I was reading Fundamental Image Processing, Chapter 5 (Image Transforms), I encountered the following problem: 
Given the arrays $x_1(m,n)$ and $x_2(m,n)$ as follows:

Write their convolution $x_3(m,n) = x_2(m,n)\ast x_1(m,n)$ as a doubly block circulant matrix operating on a vector of size 16 and calculate the result.
 Can anyone please explain what the meaning of problem is and how it should be solved?
The point is that circular convolution of two 1-D discrete signals can be expressed as the product of a circulant matrix and the vector representation of the other signal. 

The circulant matrix is a toeplitz matrix which is constructed by different circular shifts of a vector in different rows. For example, consider two signls $h[n]$ and $g[n]$, each of length $4$. If we assume $\mathbf{h}=\begin{bmatrix}
h_0 &amp; h_1 &amp; h_2 &amp; h_3
\end{bmatrix}$, then the circulant matrix denoted by $\mathrm{circ}(h)$ is $$\mathrm{circ}(\mathbf{h})=\mathbf{H}=\begin{bmatrix}
h_0 &amp; h_3 &amp; h_2 &amp; h_1\\ 
h_1 &amp; h_0 &amp; h_3 &amp; h_2\\ 
h_2 &amp; h_1 &amp; h_0 &amp; h_3\\ 
h_3 &amp; h_2 &amp; h_1 &amp; h_0
\end{bmatrix}.$$
So we can calculate $h[n]*g[n]$ by evaluating the product $\mathbf{H}\mathbf{g}$, where $\mathbf{g}=\begin{bmatrix}
g_0 &amp; g_1 &amp; g_2 &amp; g_3
\end{bmatrix}^T$. 

This can be extended to 2-D signals. I explain it through the given example.

The size of signals are $2\times2$ and $3\times3$. The size of convolution is $(2+3-1)\times(2+3-1)=4\times4$. So we need to pad zeros to adjust the size of 2-D signal $x_2(m,n)$:
$$\mathbf{A}=\begin{bmatrix}
 0 &amp; 0 &amp; 0 &amp; 0\\ 
0 &amp; -1 &amp; 0 &amp; 0\\ 
-1 &amp; 4 &amp; -1 &amp; 0\\ 
0 &amp; -1 &amp; 0 &amp; 0
\end{bmatrix}$$
Now construct four circulant matrices corresponding to the four rows of $\mathbf{A}$ denoted by $\mathbf{a}_i$ as follows:

$$\begin{align}
\mathbf{X}_0&amp;=\mathrm{circ}(\mathbf{a}_0)=\mathrm{circ}(\begin{bmatrix}
0 &amp; -1 &amp; 0 &amp; 0
\end{bmatrix})\\
\mathbf{X}_1&amp;=\mathrm{circ}(\mathbf{a}_1)=\mathrm{circ}(\begin{bmatrix}
-1 &amp; 4 &amp; -1 &amp; 0
\end{bmatrix})\\
\mathbf{X}_2&amp;=\mathrm{circ}(\mathbf{a}_2)=\mathrm{circ}(\begin{bmatrix}
0 &amp; -1 &amp; 0 &amp; 0
\end{bmatrix})\\
\mathbf{X}_3&amp;=\mathrm{circ}(\mathbf{a}_3)=\mathrm{circ}(\begin{bmatrix}
0 &amp; 0 &amp; 0 &amp; 0
\end{bmatrix})
\end{align}$$
and construct the $16\times16$ doubly circuland matrix $$\mathbf{X}=\begin{bmatrix}
\mathbf{X}_0 &amp; \mathbf{X}_3 &amp; \mathbf{X}_2 &amp; \mathbf{X}_1\\ 
\mathbf{X}_1 &amp; \mathbf{X}_0 &amp; \mathbf{X}_3 &amp; \mathbf{X}_2\\
\mathbf{X}_2 &amp; \mathbf{X}_1 &amp; \mathbf{X}_0 &amp; \mathbf{X}_3\\
\mathbf{X}_3 &amp; \mathbf{X}_2 &amp; \mathbf{X}_1 &amp; \mathbf{X}_0 
\end{bmatrix}.$$
Now we need to construct the vector corresponding to $x_1(m,n)$. Pad zeros from left and bottom to adjust indices considering $m,n$, and pad zeros from top and right to make the size $4\times4$:
$$\mathbf{B}=\begin{bmatrix}
 0 &amp; 0 &amp; 0 &amp; 0\\ 
0 &amp; 3 &amp; 4 &amp; 0\\ 
0 &amp; 1 &amp; 2 &amp; 0\\ 
0 &amp; 0 &amp; 0 &amp; 0
\end{bmatrix},$$
then construct a $16\times 1$ vector $\mathbf{b}=\begin{bmatrix}
 \mathbf{b}_0 \ |&amp; \mathbf{b}_1 \ |&amp; \mathbf{b}_2 \ |&amp;\mathbf{b}_3 
\end{bmatrix}^T$ by concatenating the rows of matrix $\mathbf{B}$. 

Finally, the 2-D circular convolution is $$\boxed{\mathbf{G}=\mathbf{X}\mathbf{b}}$$