如何利用遗传算法降低噪声图像的误差?
How to reduce the error of the noise image using genetic algorithm?
The question says, given a noise image (lena_noise) and original image (lena_original), the programmer is asked to design and implement a program that minimize the noise by given mathematical formula using Genetic algorithm.
我的问题是我的程序运行不好,真的很糟糕。这意味着当我从 lena_noise 开始时,程序应该会在一段时间后给我原始图像,但不幸的是它给出的图像比 lena_noise 差。所以我认为我的噪音程序有问题。所以,我希望找到一个关键点来找到如何使用遗传算法降低噪声?
private int[][] Performing_Noise_into_grayscale_image(Storing_Images SI)
{
this.Generate_New_Random_for_3_parameters();
int [][] nose = new int[SI.heigth][SI.width];
for (int row = 0; row < SI.heigth; row++)
{
for (int col = 0; col < SI.width; col++)
{
double no = this.NoiseAmp*sin((2*Math.PI*this.NoiseFreqCol*row) + (2*Math.PI*this.NoiseFreqCol*col));
int value = SI.Array[row][col];
int alph = (value >> 24) & 0xff;
int red = (value >> 16) & 0xff;
int green = (value >> 8) & 0xff;
int blue = value & 0xff;
int avg = (red+green+blue)/3;
alph = ((int)no) | (alph << 24);
red = ((int) no) | (avg << 16);
green = ((int) no) | (avg << 8);
blue = ((int) no) | avg;
int value2 = 0;
value2 = (alph<<24) | (red<<16) | (green<<8) | blue;
nose[row][col] = value2;
}
}
return nose;
}
函数 Generate_New_Random_for_3_parameters() 它只是一个函数,它为 3 个变量提供 0-30 之间的随机双数。这3个变量是数学公式中使用的(NoiseAmp, NoiseFreqCol, NoiseFreqCol)。
Storing_Images 有三个属性,它们是 (int [][] Array, int height, int width)
注意图片是灰度图,所以取平均值。
Brief about the program
程序有四个参数,如下:Crossover_probability = 0.3,mutation_probability = 0.05,number_of_population = 4,number_of_generations = 10。如果你对这些参数不熟悉,再看看Introduction to Genetic Algorithm。因此,程序从接收 lena_noise 开始,并在其上应用所有人口的数学公式(每个人口都有自己的随机双数数学公式),然后我们 select 最好的一个, 也就是与 lena_original 相比误差较小的那个。那么最好的就是能够为下一代生存的那个。在下一代上,我们对所有种群应用数学公式,然后我们 select 与 lena_original 图像相比,误差尽可能小的最好的。等等
Fitness函数如下,
public Storing_Images[] Fitness()
{
Storing_Images errorSSI [] = new Storing_Images[4];
for (int i = 0; i < (this.Childs.length) ; i++)
{
//choose the best one among 4
//the best one is the one that have the minimum error from the orginal image.
int [][] error = IP.Compare_OriginalImage_with_given_Image(Childs[i].Array);
errorSSI[i] = new Storing_Images(error,512,512);
}
int value=0;
int Sum [] = new int[4];
for (int i = 0; i < (this.Childs.length) ; i++)
{
for (int row = 0; row < IP.width; row++)
{
for (int col = 0; col < IP.height; col++)
{
int val = errorSSI[i].Array[row][col];
if ( val < 0 )
val = val * -1;
value = value + val;
}
}
double x = (value/(IP.width * IP.height));
Sum[i] = (int) x;
value =0;
}
int Mini=Sum[0];
int posi_of_mini= 0;
// int pos2=0;
for (int k = 0; k < Sum.length; k++)
{
if ( Sum[k] < Mini )
{
// pos2 = Mini;
Mini = Sum[k];
posi_of_mini = k;
}
}
this.Childs[0] = Childs[posi_of_mini];
this.Childs[1] = Childs[posi_of_mini];
this.Childs[2] = Childs[posi_of_mini];
this.Childs[3] = Childs[posi_of_mini];
return Childs;
}
关于 lena_original 和人口之一之间的比较功能。
public int [][] Compare_OriginalImage_with_given_Image(int [][] current)
{
int [][] error = new int [this.height][this.width];
for (int row = 0; row < height; row++)
{
for (int col = 0; col < width; col++)
{
int value1 = this.Orginal[row][col];
int value2 = current[row][col];
//I need to put absolute value for either value 1 and value2
if (value1 < 0)
value1 = value1 *-1;
if(value2 < 0)
value2 = value2 * -1;
//int min = Math.min(value1, value2);
int er = value2 - value1;
error[row][col] = er;
}
}
return error;
}
Reference.
*与我的问题类似的问题,但没有关于答案的详细信息来自此页面 Image processing using genetic algorithm
*How to convert a color image into grayscale image in Java 这几页告诉我们如何处理灰度图像并使用它。
随时提出有关问题的问题。此外,欢迎任何评论、提示等。谢谢
试试这个来创建灰度图像:
public static void makeGray(BufferedImage img)
{
for (int x = 0; x < img.getWidth(); x++) {
for (int y = 0; y < img.getHeight(); y++) {
int rgb = img.getRGB(x, y);
int r = (rgb >> 16) & 0xFF;
int g = (rgb >> 8) & 0xFF;
int b = (rgb & 0xFF);
int grayLevel = (r + g + b) / 3;
int gray = (grayLevel << 16) + (grayLevel << 8) + grayLevel;
img.setRGB(x, y, gray);
}
}
}
现在,您需要一种方法来组合人口中的 2 个元素。
怎么样都无所谓,所以为了示例,我将图像平均分割:
for (int x = 0; x < img1.getWidth() / 2; x++)
for (int y = 0; y < img1.getHeight(); y++)
// build 1st half of the result image
for (int x = img2.getWidth() / 2; x < img2.getWidth(); x++)
for (int y = 0; y < img2.getHeight(); y++)
// build 2nd half of the result image
您还需要考虑突变率,它可以改变结果图像上的随机元素。 (稍后再做)
GA是一种全局优化算法。用于图像去噪不是很方便。即使你找到了合适的公式,它也只适用于这对图像。
The question says, given a noise image (lena_noise) and original image (lena_original), the programmer is asked to design and implement a program that minimize the noise by given mathematical formula using Genetic algorithm.
我的问题是我的程序运行不好,真的很糟糕。这意味着当我从 lena_noise 开始时,程序应该会在一段时间后给我原始图像,但不幸的是它给出的图像比 lena_noise 差。所以我认为我的噪音程序有问题。所以,我希望找到一个关键点来找到如何使用遗传算法降低噪声?
private int[][] Performing_Noise_into_grayscale_image(Storing_Images SI)
{
this.Generate_New_Random_for_3_parameters();
int [][] nose = new int[SI.heigth][SI.width];
for (int row = 0; row < SI.heigth; row++)
{
for (int col = 0; col < SI.width; col++)
{
double no = this.NoiseAmp*sin((2*Math.PI*this.NoiseFreqCol*row) + (2*Math.PI*this.NoiseFreqCol*col));
int value = SI.Array[row][col];
int alph = (value >> 24) & 0xff;
int red = (value >> 16) & 0xff;
int green = (value >> 8) & 0xff;
int blue = value & 0xff;
int avg = (red+green+blue)/3;
alph = ((int)no) | (alph << 24);
red = ((int) no) | (avg << 16);
green = ((int) no) | (avg << 8);
blue = ((int) no) | avg;
int value2 = 0;
value2 = (alph<<24) | (red<<16) | (green<<8) | blue;
nose[row][col] = value2;
}
}
return nose;
}
函数 Generate_New_Random_for_3_parameters() 它只是一个函数,它为 3 个变量提供 0-30 之间的随机双数。这3个变量是数学公式中使用的(NoiseAmp, NoiseFreqCol, NoiseFreqCol)。
Storing_Images 有三个属性,它们是 (int [][] Array, int height, int width)
注意图片是灰度图,所以取平均值。
Brief about the program
程序有四个参数,如下:Crossover_probability = 0.3,mutation_probability = 0.05,number_of_population = 4,number_of_generations = 10。如果你对这些参数不熟悉,再看看Introduction to Genetic Algorithm。因此,程序从接收 lena_noise 开始,并在其上应用所有人口的数学公式(每个人口都有自己的随机双数数学公式),然后我们 select 最好的一个, 也就是与 lena_original 相比误差较小的那个。那么最好的就是能够为下一代生存的那个。在下一代上,我们对所有种群应用数学公式,然后我们 select 与 lena_original 图像相比,误差尽可能小的最好的。等等
Fitness函数如下,
public Storing_Images[] Fitness()
{
Storing_Images errorSSI [] = new Storing_Images[4];
for (int i = 0; i < (this.Childs.length) ; i++)
{
//choose the best one among 4
//the best one is the one that have the minimum error from the orginal image.
int [][] error = IP.Compare_OriginalImage_with_given_Image(Childs[i].Array);
errorSSI[i] = new Storing_Images(error,512,512);
}
int value=0;
int Sum [] = new int[4];
for (int i = 0; i < (this.Childs.length) ; i++)
{
for (int row = 0; row < IP.width; row++)
{
for (int col = 0; col < IP.height; col++)
{
int val = errorSSI[i].Array[row][col];
if ( val < 0 )
val = val * -1;
value = value + val;
}
}
double x = (value/(IP.width * IP.height));
Sum[i] = (int) x;
value =0;
}
int Mini=Sum[0];
int posi_of_mini= 0;
// int pos2=0;
for (int k = 0; k < Sum.length; k++)
{
if ( Sum[k] < Mini )
{
// pos2 = Mini;
Mini = Sum[k];
posi_of_mini = k;
}
}
this.Childs[0] = Childs[posi_of_mini];
this.Childs[1] = Childs[posi_of_mini];
this.Childs[2] = Childs[posi_of_mini];
this.Childs[3] = Childs[posi_of_mini];
return Childs;
}
关于 lena_original 和人口之一之间的比较功能。
public int [][] Compare_OriginalImage_with_given_Image(int [][] current)
{
int [][] error = new int [this.height][this.width];
for (int row = 0; row < height; row++)
{
for (int col = 0; col < width; col++)
{
int value1 = this.Orginal[row][col];
int value2 = current[row][col];
//I need to put absolute value for either value 1 and value2
if (value1 < 0)
value1 = value1 *-1;
if(value2 < 0)
value2 = value2 * -1;
//int min = Math.min(value1, value2);
int er = value2 - value1;
error[row][col] = er;
}
}
return error;
}
Reference.
*与我的问题类似的问题,但没有关于答案的详细信息来自此页面 Image processing using genetic algorithm
*How to convert a color image into grayscale image in Java 这几页告诉我们如何处理灰度图像并使用它。
随时提出有关问题的问题。此外,欢迎任何评论、提示等。谢谢
试试这个来创建灰度图像:
public static void makeGray(BufferedImage img)
{
for (int x = 0; x < img.getWidth(); x++) {
for (int y = 0; y < img.getHeight(); y++) {
int rgb = img.getRGB(x, y);
int r = (rgb >> 16) & 0xFF;
int g = (rgb >> 8) & 0xFF;
int b = (rgb & 0xFF);
int grayLevel = (r + g + b) / 3;
int gray = (grayLevel << 16) + (grayLevel << 8) + grayLevel;
img.setRGB(x, y, gray);
}
}
}
现在,您需要一种方法来组合人口中的 2 个元素。 怎么样都无所谓,所以为了示例,我将图像平均分割:
for (int x = 0; x < img1.getWidth() / 2; x++)
for (int y = 0; y < img1.getHeight(); y++)
// build 1st half of the result image
for (int x = img2.getWidth() / 2; x < img2.getWidth(); x++)
for (int y = 0; y < img2.getHeight(); y++)
// build 2nd half of the result image
您还需要考虑突变率,它可以改变结果图像上的随机元素。 (稍后再做)
GA是一种全局优化算法。用于图像去噪不是很方便。即使你找到了合适的公式,它也只适用于这对图像。