Image editing software like Photoshop provide many image
effects like blur, sharpen, and edge detection. These effects
are commonly implemented through a kernel, a matrix describing
a certain image effect. The kernel is applied to an image
through convolution: flipping both the rows and columns of the
kernel and then multiplying locationally similar entries and
summing.
For example, letâ€™s say we have the $4 \times 4$ image $ \begin{bmatrix} a & b & c & d
\\ e & f & g & h \\ i & j & k & l \\ m
& n & o & p \end{bmatrix} $ and the
$2 \times 2$ kernel
$ \begin{bmatrix} 1 & 2 \\ 3
& 4 \end{bmatrix}. $
After convolution, the resulting $3 \times 3$ image would
be:
\[ \begin{bmatrix}
4a+3b+2e+1f & 4b+3c+2f+1g & 4c+3d+2g+1h \\ 4e+3f+2i+1j
& 4f+3g+2j+1k & 4g+3h+2k+1l \\ 4i+3j+2m+1n &
4j+3k+2n+1o & 4k+3l+2o+1p \end{bmatrix} \]
A realworld example of this is the blur effect:

\[ \Rightarrow \frac{1}{9}
\begin{bmatrix} 1 & 1 & 1 \\ 1 & 1 & 1 \\
1 & 1 & 1 \end{bmatrix} \Rightarrow
\] 

Photo
by Michael Plotke cc bysa 3.0
Input
The first line of the input contains four integers,
$H$, the height of the
image, $W$, the width of
the image, $N$, the height
of the kernel, and $M$,
the width of the kernel ($1 \leq
H \leq 20$, $1 \leq W \leq
20$, $1 \leq N \leq
H$, $1 \leq M \leq
W$).
Each of the next $H$
lines contains $W$
integers, between $0$ and
$100$ inclusive,
representing the image.
Each of the next $N$
lines contains $M$
integers, between $0$ and
$100$ inclusive,
representing the kernel.
Output
Output the resulting image after convolution, consisting of
$HN+1$ lines, each with
$WM+1$ integers.
Sample Input 1 
Sample Output 1 
4 4 2 2
1 2 3 4
5 6 7 8
9 10 11 12
13 14 15 16
1 2
3 4

26 36 46
66 76 86
106 116 126
