/*******************************************************

* Copyright (c) 2018, ArrayFire

* All rights reserved.

*

* This file is distributed under 3-clause BSD license.

* The complete license agreement can be obtained at:

* http://arrayfire.com/licenses/BSD-3-Clause

********************************************************/

#include <arrayfire.h>

#include <stdio.h>

#include <af/util.h>

#include <cstdlib>

using namespace af;

const unsigned ITERATIONS = 96;

const float RELAXATION_FACTOR = 0.05f;

array normalize(const array &in) {

float mx = max<float>(in.as(f32));

float mn = min<float>(in.as(f32));

return (in - mn) / (mx - mn);

}

int main(int argc, char *argv[]) {

int device = argc > 1 ? atoi(argv[1]) : 0;

try {

af::setDevice(device);

af::info();

printf("** ArrayFire Image Deconvolution Demo **\n");

af::Window myWindow("Image Deconvolution");

array in = loadImage(ASSETS_DIR "/examples/images/house.jpg", false);

array kernel = gaussianKernel(13, 13, 2.25, 2.25);

array blurred = convolve(in, kernel);

array tikhonov =

inverseDeconv(blurred, kernel, 0.05, AF_INVERSE_DECONV_TIKHONOV);

array landweber =

iterativeDeconv(blurred, kernel, ITERATIONS, RELAXATION_FACTOR,

AF_ITERATIVE_DECONV_LANDWEBER);

array richlucy =

iterativeDeconv(blurred, kernel, ITERATIONS, RELAXATION_FACTOR,

AF_ITERATIVE_DECONV_RICHARDSONLUCY);

while (!myWindow.close()) {

myWindow.grid(2, 3);

myWindow(0, 0).image(normalize(in), "Input Image");

myWindow(1, 0).image(normalize(blurred), "Blurred Image");

myWindow(0, 1).image(normalize(tikhonov), "Tikhonov");

myWindow(1, 1).image(normalize(landweber), "Landweber");

myWindow(0, 2).image(normalize(richlucy), "Richardson-Lucy");

myWindow.show();

}

} catch (af::exception &e) {

fprintf(stderr, "%s\n", e.what());

throw;

}

return 0;

}