Multi-spectral Imaging¶

Important: Please read the installation page for details about how to install the toolboxes. $\newcommand{\dotp}[2]{\langle #1, #2 \rangle}$ $\newcommand{\enscond}[2]{\lbrace #1, #2 \rbrace}$ $\newcommand{\pd}[2]{ \frac{ \partial #1}{\partial #2} }$ $\newcommand{\umin}[1]{\underset{#1}{\min}\;}$ $\newcommand{\umax}[1]{\underset{#1}{\max}\;}$ $\newcommand{\umin}[1]{\underset{#1}{\min}\;}$ $\newcommand{\uargmin}[1]{\underset{#1}{argmin}\;}$ $\newcommand{\norm}[1]{\|#1\|}$ $\newcommand{\abs}[1]{\left|#1\right|}$ $\newcommand{\choice}[1]{ \left\{ \begin{array}{l} #1 \end{array} \right. }$ $\newcommand{\pa}[1]{\left(#1\right)}$ $\newcommand{\diag}[1]{{diag}\left( #1 \right)}$ $\newcommand{\qandq}{\quad\text{and}\quad}$ $\newcommand{\qwhereq}{\quad\text{where}\quad}$ $\newcommand{\qifq}{ \quad \text{if} \quad }$ $\newcommand{\qarrq}{ \quad \Longrightarrow \quad }$ $\newcommand{\ZZ}{\mathbb{Z}}$ $\newcommand{\CC}{\mathbb{C}}$ $\newcommand{\RR}{\mathbb{R}}$ $\newcommand{\EE}{\mathbb{E}}$ $\newcommand{\Zz}{\mathcal{Z}}$ $\newcommand{\Ww}{\mathcal{W}}$ $\newcommand{\Vv}{\mathcal{V}}$ $\newcommand{\Nn}{\mathcal{N}}$ $\newcommand{\NN}{\mathcal{N}}$ $\newcommand{\Hh}{\mathcal{H}}$ $\newcommand{\Bb}{\mathcal{B}}$ $\newcommand{\Ee}{\mathcal{E}}$ $\newcommand{\Cc}{\mathcal{C}}$ $\newcommand{\Gg}{\mathcal{G}}$ $\newcommand{\Ss}{\mathcal{S}}$ $\newcommand{\Pp}{\mathcal{P}}$ $\newcommand{\Ff}{\mathcal{F}}$ $\newcommand{\Xx}{\mathcal{X}}$ $\newcommand{\Mm}{\mathcal{M}}$ $\newcommand{\Ii}{\mathcal{I}}$ $\newcommand{\Dd}{\mathcal{D}}$ $\newcommand{\Ll}{\mathcal{L}}$ $\newcommand{\Tt}{\mathcal{T}}$ $\newcommand{\si}{\sigma}$ $\newcommand{\al}{\alpha}$ $\newcommand{\la}{\lambda}$ $\newcommand{\ga}{\gamma}$ $\newcommand{\Ga}{\Gamma}$ $\newcommand{\La}{\Lambda}$ $\newcommand{\si}{\sigma}$ $\newcommand{\Si}{\Sigma}$ $\newcommand{\be}{\beta}$ $\newcommand{\de}{\delta}$ $\newcommand{\De}{\Delta}$ $\newcommand{\phi}{\varphi}$ $\newcommand{\th}{\theta}$ $\newcommand{\om}{\omega}$ $\newcommand{\Om}{\Omega}$

This numerical tour explores multi-spectral image processing.

In [2]:

addpath('toolbox_signal')
addpath('toolbox_general')
addpath('solutions/multidim_3_multispectral')

The multispectral image used in this tour is taken from the database of <http://www2.cmp.uea.ac.uk/Research/compvis/MultiSpectralDB.htm Hordley, Finlayson, Morovic> You can test the methods developped in this tour on other images.

Scilab user should increase memory. WARNING: This should be done only once.

In [3]:

extend_stack_size(10);

Multi-spectral Images¶

A multi-spectral image is a |(n,p,q)| cube of data, where |(n,p)| is the size of the image, and |q| is the number of spectral samples, ranging from infra-red to ultra-violet. The RGB channels are located approximately at samples locations |[10 15 20]|

We load a multispectral image.

In [4]:

name = 'unclebens';
options.nbdims = 3;
M = read_bin(name, options);
n = 256;
M = rescale( crop(M,[n n size(M,3)]) );

width of the image

In [5]:

n = size(M,1);

number of spectral components

In [6]:

p = size(M,3);

Display a few channels of the image.

In [7]:

clf;
imageplot(M(:,:,10), 'R', 1,3,1);
imageplot(M(:,:,15), 'G', 1,3,2);
imageplot(M(:,:,20), 'B', 1,3,3);

Display an approximate RGB image.

In [8]:

rgbsel = [10 15 20];
clf;
imageplot(M(:,:,rgbsel), 'RGB');

Display the spectral content of a given pixel. As you can see, spectral curves are quite smooth.

pixel location

In [9]:

pos = [30 50];

spectral content

In [10]:

v = M(pos(1),pos(2),:); v = v(:);
clf;
plot( v, '.-');
axis('tight');

Multi-Spectral Image Compression¶

To perform the compression / approximation of the full cube of data, one needs to use a 3D transformation of the cube. One can use a truely 3D wavelet transform, or the combination (tensor product) of a 2D wavelet transform and a cosine transform.

A 1D DCT is first applied to each spectral content.

In [11]:

U = reshape( M, [n*n p] )';
U = dct(U);
U = reshape(U', [n n p]);

We plot the spectral content of a pixel and its DCT transform. You can note that the DCT coefficients are quikcly decaying.

In [12]:

clf;
subplot(2,1,1);
v = M(pos(1),pos(2),:); v = v(:);
plot(v); axis('tight');
title('Spetral content');
subplot(2,1,2);
v = U(pos(1),pos(2),:); v = v(:);
plot(v); axis('tight');
title('DCT tranform');

As the frequenc index |i| increase, the DCT component |U(:,:,i)| becomes small and noisy. Note that |U(:,:,1)| is the average of the spectral components.

In [13]:

ilist = [1 4 8 16];
clf;
for i=1:length(ilist);
    imageplot(U(:,:,ilist(i)), ['DCT freq ' num2str(ilist(i))], 2,2,i);
end