# IMAGE PROCESSING

" Two roads diverged in a wood, and I,
I took the one less traveled by,
And that has made all the difference "-Robert Frost

### Multilook Technique for speckle reduction

Consider a stack of images  affected by speckle noise of a same scene.

In order to reduce the speckle, the availability of the stack of images can be utilized to obtain speckle reduced image.
In order to understand the multilook technique, let’s first generate noisy images and then apply speckle reduction.

Generate stack of noisy images:
1.      Read a noise free image
2.      Generate exponential noise with mean 1 and multiply it with the noise free image

3.      Repeat the process of generating exponential noise and multiplying  it with the noise free image to create stack of ‘n’ images

MATLAB code:
%Multilook - Speckle reduction

I = double(I);
figure,imagesc(I);colormap(gray);title('Original Image');

[m, n] = size(I);
numofimg = 9;
Stack = zeros([m n numofimg]);

for i = 1:numofimg
%Generate exponential noise
noise=random('exp',1,m,n);

%Multiply Noise free Image with noise
Stack(:,:,i)=I.*noise;

end

Explanation:

Random noise with exponential distribution is multiplied with the noise free image to generate image affected by speckle.

Fig: Probability Density Function (PDF) of the noise generated for ‘n’ images that follows exponential distribution.

Multilook(ML) Technique:
Multilook image is the average of the stack of images. This technique is used widely in the field of Synthetic Aperture Radar(SAR) to reduce speckle on the same scene but different acquisition periods.
The stack of data is created for the same scene but with different time of acquisition and ML is applied to reduce the speckle considerably since the noise on the single image will be quite high.

MATLAB code:
ML_image = mean(Stack,3);
figure,imagesc(Stack(:,:,1));colormap(gray);title('Single Noisy Image');
figure,imagesc(ML_image);colormap(gray);title('Multilook Image');

Explanation:

For instance, in the above shown image, consider the red dot as the pixel value of the first pixel position of each image in the stack. Add all the pixel values represented in red dot and divide by the number of images in the stack. Similarly, perform the average for the second pixel position (green dot) and so on and so forth for the whole image.

Matlab code ‘mean(Stack,3) ‘ finds the mean of the image in 3rd dimension and the final result is the Multilooked Image with reduced speckle.

Moving Average Filter:
In order to reduce speckle further, a moving average filter can be used.
Moving average filter of window size 3x3 and 5x5 is applied on the multilook image and window of size 3x3 on a single speckled image in order to appreciate the multilook technique performance.

MATLAB code:

box = ones(3)/9;
ML_avg3 = conv2(ML_image,box,'same');
figure,imagesc(ML_avg3);colormap(gray);title('Moving Average - Window 3 x 3');
box = ones(5)/25;
ML_avg5 = conv2(ML_image,box,'same');
figure,imagesc(ML_avg5);colormap(gray);title('Moving Average - Window 5 x 5');
box = ones(3)/9;
avg_flt = conv2(Stack(:,:,1),box,'same');
figure,imagesc(avg_flt);colormap(gray);title('Moving Average - Single Image');

Like "IMAGE PROCESSING" page