CODEWITHSUNDEEP
matlabimage-processingconvolutionsliding-window
Bharath S
Bharath S

Reputation: 161

Sliding window for matlab

The intention is to estimate in a [3 3] sliding window. 0.5*[(A(i)-A(5))^2] is computed where a(i) is the pixels around center pixel a(5).

The mean of each of these 8 half square differences is stored in the center pixel's location.

To tackle this conv2 and nlfilter were used on a training example matrix as follows.

clc;
close all;
clear all;
A = [1 2 3 4 5 6;5 4 6 3 2 1;2 3 2 1 4 5];
kernel = [-1 -1 -1; -1 8 -1; -1 -1 -1];
outputImage = conv2(A, kernel);
fun = @(x) mean(x(:));
B= nlfilter (outputImage,[3 3],fun);

The initial idea was to calculate the difference and store it in the location of the pixels instead of the center pixel. Then use a sliding window to take mean of those differences and replace the center pixel.

It was apparent that my logic was flawed.

Though i was able to calculate the difference(i first tried to see simple difference was possible for me) i had to deal with data being overwritten. moreover this method will create a matrix larger than the original which is also wrong.

Upvotes: 2

Views: 1030

Answers (1)

Shai
Shai

Reputation: 114816

the function mean and the kernel you are using are both linear and do not represent the non-linear operation you are trying to achieve.

One way of using conv and mean is by computing the 8 differences as different output channels

ker = cell(1,8);
for ii=1:8
    ker{ii} = zeros(3);
    ker{ii}(2,2) = 1; %// for a(5)
    if ii < 5
       ker{ii}(ii) = -1; 
    else
       ker{ii}(ii+1) = -1; 
    end
 end
 interim = zeros( [size(A,1) size(A,2), numel(ker)] ); % allocate room for intermidiate results
 for ii=1:numel(ker)
     interim(:,:,ii) = conv2(A, ker{ii}, 'same' ); %//'same' takes care of output size for you
 end

Now interim holds each of the different a(5)-a(i) we are ready for the non-linear operation

 interim = interim.^2;
 B = 0.5 * interim;

Upvotes: 2

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