Question

I am searching for an algorithm or C++/Matlab library that can be used to separate two images multiplied together. A visual example of this problem is given below.

Image 1 can be anything (such as a relatively complicated scene). Image 2 is very simple, and can be mathematically generated. Image 2 always has similar morphology (i.e. downward trend). By multiplying Image 1 by Image 2 (using point-by-point multiplication), we get a transformed image.

Given only the transformed image, I would like to estimate Image 1 or Image 2. Is there an algorithm that can do this?

Here are the Matlab code and images:

load('trans.mat');
imageA = imread('room.jpg');
imageB = abs(response);  % loaded from MAT file

[m,n] = size(imageA);
image1 = rgb2gray( imresize(im2double(imageA), [m n]) );
image2 = imresize(im2double(imageB), [m n]);

figure; imagesc(image1); colormap gray; title('Image 1 of Room')
colorbar

figure; imagesc(image2); colormap gray; title('Image 2 of Response')
colorbar

% This is image1 and image2 multiplied together (point-by-point)
trans = image1 .* image2;
figure; imagesc(trans); colormap gray; title('Transformed Image')
colorbar

UPDATE

There are a number of ways to approach this problem. Here are the results of my experiments. Thank you to all who responded to my question!

1. Low-pass filtering of image

As noted by duskwuff, taking the low-pass filter of the transformed image returns an approximation of Image 2. In this case, the low-pass filter has been Gaussian. You can see that it is possible to identify multiplicative noise in the image using the low-pass filter.

2. Homomorphic Filtering

As suggested by EitenT I examined homomorphic filtering. Knowing the name of this type of image filtering, I managed to find a number of references that I think would be useful in solving similar problems.

1. S. P. Banks, Signal processing, image processing, and pattern recognition. New York: Prentice Hall, 1990.

2. A. Oppenheim, R. Schafer, and J. Stockham, T., “Nonlinear filtering of multiplied and convolved signals,” IEEE Transactions on Audio and Electroacoustics, vol. 16, no. 3, pp. 437 – 466, Sep. 1968.

3. Blind image Deconvolution: theory and applications. Boca Raton: CRC Press, 2007.

Chapter 5 of the Blind image deconvolution book is particularly good, and contains many references to homomorphic filtering. This is perhaps the most generalized approach that will work well in many different applications.

3. Optimization using fminsearch

As suggested by Serg, I used an objective function with fminsearch. Since I know the mathematical model of the noise, I was able to use this as input to an optimization algorithm. This approach is entirely problem-specific, and may not be always useful in all situations.

Here is a reconstruction of Image 2:

Here is a reconstruction of Image 1, formed by dividing by the reconstruction of Image 2:

Here is the image containing the noise:

Source code

Here is the source code for my problem. As shown by the code, this is a very specific application, and will not work well in all situations.

N = 1001;
q = zeros(N, 1);
q(1:200) = 55;
q(201:300) = 120;
q(301:400) = 70;
q(401:600) = 40;
q(601:800) = 100;
q(801:1001) = 70;
dt = 0.0042;
fs = 1 / dt;
wSize = 101;
Glim = 20;
ginv = 0;
[R, ~, ~] = get_response(N, q, dt, wSize, Glim, ginv);
rows = wSize;
cols = N;
cut_val = 200;

figure; imagesc(abs(R)); title('Matrix output of algorithm')
colorbar

figure;
imagesc(abs(R)); title('abs(response)')

figure;
imagesc(imag(R)); title('imag(response)')

imageA = imread('room.jpg');

% images should be of the same size
[m,n] = size(R);
image1 =  rgb2gray( imresize(im2double(imageA), [m n]) );


% here is the multiplication (with the image in complex space)
trans = ((image1.*1i)) .* (R(end:-1:1, :));


figure;
imagesc(abs(trans)); colormap(gray);


% take the imaginary part of the response
imagLogR = imag(log(trans)); 


% The beginning and end points are not usable
Mderiv = zeros(rows, cols-2);
for k = 1:rows
   val = deriv_3pt(imagLogR(k,:), dt);
   val(val > cut_val) = 0;
   Mderiv(k,:) = val(1:end-1);
end


% This is the derivative of the imaginary part of R
% d/dtau(imag((log(R)))
% Do we need to remove spurious values from the matrix?
figure; 
imagesc(abs(log(Mderiv)));


disp('Running iteration');
% Apply curve-fitting to get back the values
% by cycling over the cols
q0 = 10;
q1 = 500;
NN = cols - 2;
qout = zeros(NN, 1);
for k = 1:NN
    data = Mderiv(:,k); 
    qout(k) = fminbnd(@(q) curve_fit_to_get_q(q, dt, rows, data),q0,q1);
end


figure; plot(q); title('q value input as vector'); 
ylim([0 200]); xlim([0 1001])

figure;
plot(qout); title('Reconstructed q')
ylim([0 200]); xlim([0 1001])

% make the vector the same size as the other
qout2 = [qout(1); qout; qout(end)];

% get the reconstructed response
[RR, ~, ~] = get_response(N, qout2, dt, wSize, Glim, ginv);
RR = RR(end:-1:1,:);

figure; imagesc(abs(RR)); colormap gray 
title('Reconstructed Image 2')
colorbar; 

% here is the reconstructed image of the room
% NOTE the division in the imagesc function
check0 = image1 .* abs(R(end:-1:1, :));
figure; imagesc(check0./abs(RR)); colormap gray
title('Reconstructed Image 1')
colorbar; 

figure; imagesc(check0); colormap gray
title('Original image with noise pattern')
colorbar; 

function [response, L, inte] = get_response(N, Q, dt, wSize, Glim, ginv)

fs = 1 / dt; 
Npad = wSize - 1; 
N1 = wSize + Npad;
N2 = floor(N1 / 2 + 1);
f = (fs/2)*linspace(0,1,N2);
omega = 2 * pi .* f';
omegah = 2 * pi * f(end);
sigma2 = exp(-(0.23*Glim + 1.63));

sign = 1;
if(ginv == 1)
    sign = -1;
end

ratio = omega ./ omegah;
rs_r = zeros(N2, 1);  
rs_i = zeros(N2, 1);   
termr = zeros(N2, 1);
termi = zeros(N2, 1);
termr_sub1 = zeros(N2, 1);
termi_sub1 = zeros(N2, 1);
response = zeros(N2, N);
L = zeros(N2, N);
inte = zeros(N2, N);

 % cycle over cols of matrix
for ti = 1:N               

    term0 = omega ./ (2 .* Q(ti));
    gamma = 1 / (pi * Q(ti));

    % calculate for the real part
    if(ti == 1)
        Lambda = ones(N2, 1);
        termr_sub1(1) = 0;  
        termr_sub1(2:end) = term0(2:end) .* (ratio(2:end).^-gamma);  
    else
        termr(1) = 0; 
        termr(2:end) = term0(2:end) .* (ratio(2:end).^-gamma); 
        rs_r = rs_r - dt.*(termr + termr_sub1);
        termr_sub1 = termr;
        Beta = exp( -1 .* -0.5 .* rs_r );

        Lambda = (Beta + sigma2) ./ (Beta.^2 + sigma2);  % vector
    end 

    % calculate for the complex part  
    if(ginv == 1)  
        termi(1) = 0;
        termi(2:end) = (ratio(2:end).^(sign .* gamma) - 1) .* omega(2:end);
    else
        termi = (ratio.^(sign .* gamma) - 1) .* omega;
    end
    rs_i = rs_i - dt.*(termi + termi_sub1);
    termi_sub1 = termi;
    integrand = exp( 1i .* -0.5 .* rs_i );

    L(:,ti) = Lambda;
    inte(:,ti) = integrand;

    if(ginv == 1) 
        response(:,ti) = Lambda .* integrand;
    else        
        response(:,ti) = (1 ./ Lambda) .* integrand;
    end  
end % ti loop

function sse = curve_fit_to_get_q(q, dt, rows, data)

% q = trial q value
% dt = timestep
% rows = number of rows
% data = actual dataset

fs = 1 / dt;
N2 = rows;
f = (fs/2)*linspace(0,1,N2);  % vector for frequency along cols
omega = 2 * pi .* f';
omegah = 2 * pi * f(end);
ratio = omega ./ omegah;

gamma = 1 / (pi * q);

% calculate for the complex part  
termi = ((ratio.^(gamma)) - 1) .* omega;

% for now, just reverse termi 
termi = termi(end:-1:1);
% 

% Do non-linear curve-fitting

% termi is a column-vector with the generated noise pattern 
% data is the log-transformed image
% sse is the value that is returned to fminsearchbnd
Error_Vector =  termi - data;
sse = sum(Error_Vector.^2);

function output = deriv_3pt(x, dt)

N = length(x);
N0 = N - 1;
output = zeros(N0, 1);
denom = 2 * dt;

for k = 2:N0 
   output(k - 1) = (x(k+1) - x(k-1)) / denom;  
end

Answer 1

I would try constrained optimization (fmincon in Matlab).
If you understand the source / nature of the 2-nd image, you probably can define a multivariate function that generates similar noise patterns. The objective function can be the correlation between the generated noise image, and the last image.