demo.asv

% Authors : Abhiram Gnanasambandam (agnanasa@purdue.edu),
%Stanley H. Chan (stanchan@purdue.edu)
%Intelligent Imaging Lab, Dept. of ECE, Purdue University
% Reference:
%[1]HDR Imaging with Quanta Image Sensors: Theoretical Limits and Optimal
%Reconstruction, Abhiram Gnanasambandam and Stanley H. Chan, 
%IEEE Transactions on Computational Imaging, 2020
%[2]High Dynamic Range Imaging using Quanta Image Sensors, Abhiram
%Gnansambandam, Jiaju Ma, Stanley H. Chan, IISW 2019.

%%
clear all;
close all;
addpath('Simulation/Matlab')
%%
%Parameters
T = 10;
sigmaRN = 0.25;
Nbits = 3;
denoise = 1; %1 if you want to denoise, 0 if you dont

%%
load('B_snr.mat'); %Saved values for SigmaRN = 0.25, Nbits = 3 Should re-run
%                   this section after uncommenting the following lines, if
%                   you want different Nbits and sigmaRN


%This step has to run once for each different setting and the variables has
%to be stored. An efficicent hardware solution will be store these values
%in a look-up table
% p = [0.0228 0.9544 0.0228]; % Only for sigmaRN = 0.25. Has to change for different read noise 
% count = 1;
% for i = 0.0001:0.0001:25
% 
%     m(count) = mean_mu(2^Nbits - 1,i,p);
%     count = count + 1;
% end
% 
% B = fit(m',[0.0001:0.0001:25]','linearinterp');
% 
% 
% count = 1;
% for i = 0.0001:0.0001:25
% 
%     m(count) = SNRH(2^Nbits - 1,i,1000,p);
%     count = count + 1;
% end
% 
% snr = fit([0.0001:0.0001:25]',m','linearinterp');
%%
%Simulate multiple LDR images
alpha = [1,1/10,1/100]; %Relative integration times


% for i = 1:
A = hdrread('cars1.hdr');
A = mean(A,3); %Convert to grayscale

A = imresize(A,0.25,'nearest');
A(A<0) = 0;
A(A>1e17) = 1e17;

b = A;
b(A==0) = 1e-16;

loga = log(b);

logamin = min(loga(:));
logamax = max(loga(:));

loga = (loga - logamin)./(logamax - logamin);

loga = loga * (log(8000) - log(0.01));

loga = loga + log(0.01);

b = exp(loga);

b(A==0) = min(b(:));


im1 =  squeeze(sum(simulate_noisy_images_lite(alpha(1)*b,sigmaRN,T,Nbits),1)); 

IM1 = LDR_reconstruction(im1,sigmaRN,T,B,denoise);

im2 =  squeeze(sum(simulate_noisy_images_lite(alpha(2)*b,sigmaRN,T,Nbits),1)); 

IM2 = LDR_reconstruction(im2,sigmaRN,T,B,denoise);

im3 = squeeze(sum(simulate_noisy_images_lite(alpha(3)*b,sigmaRN,T,Nbits),1)); 

IM3 = LDR_reconstruction(im3,sigmaRN,T,B,denoise);
%%
%HDR reconstruction
rec = HDR_rec(IM1,IM2,IM3,alpha,snr);
rec = reshape(rec,size(A));
figure;


subplot(2,2,1);imshow(IM1);
subplot(2,2,2);imshow(IM2);
subplot(2,2,3);imshow(IM3);
subplot(2,2,4);imshow(tonemap(single(rec)));