數學原理:
首先看兩張圖檔,大小均為256 * 256個像素, 第一張是純藍色
圖一:
第二張是加有随機噪聲的藍色
圖二:
産生随機噪聲的算法簡單的不能再簡單了
假設rgb的r與g顔色分量均為零, 則 blue = 255 * math.random() 随機數的取值範圍在
[0, 1]之間, 程式的核心代碼如下:
for(int row=0; row<256; row++) {
for(int col=0; col<256; col++) {
b = (int)(255.0d * math.random());
rgbdata[index]= ((clamp(a) & 0xff) << 24) |
((clamp(r)& 0xff) << 16) |
((clamp(g)& 0xff) << 8) |
((clamp(b)& 0xff));
index++;
}
}
上面顯然不是我想要的結果,我想要的是下面兩種:
圖三:
圖四:
對的,隻要我們對上面的算法稍加改進,就可以實作這樣漂亮的噪聲效果
實作第二張圖效果的算法缺點在于,它每次都産生一個新的随機數,假設[0,1] = 255,接着第
二點随機可以能為[0, 2] = 0 第三個點可能随機值為[0, 3] = 125, 毫無規律可言,而我希望是
假設第一點随機[0, 1] = 255則間隔n個點以後再産生下個随機顔色值[0,n+1] =125, 在下一
個點則為[0, 2n +1] = 209…..于是問題産生了, 我們怎麼計算[1, n]的之間的每個像素點的值
哇,這個問題不正是關于圖像放縮的插值問題嘛,一個最簡單的選擇是雙線性插值算法,
有了算法選擇,下面的問題就是我們怎麼計算點值的問題,面臨兩個選擇,一個值照搬雙線
性插值中的計算方法,但是有點不自然,我們想要的是噪聲,顯然線性的計算結果不是最好
的最好的選擇,cos(x)如何,在[0, pi]内是遞減,在[pi,2pi]内是遞增,而且值的範圍在[-1, 1]
之間,而我們的随機數值要在[0, 1]之間于是綜合上述考慮我們有cos(pi + (x-x0/x1-x0)* pi) + 1, 現
在計算出來的值是[0, 1]區間之内 根據插值公式最終有:
y= (y1-y0) * cos(pi + (x-x0/x1-x0) * pi) + 1 + y0
其中[x, y]代表要計算的點,周圍四個采樣點為:[x-n, y-n], [x+n,y-n], [x-n, y+n], [x+n, y+n ]
運用雙線性插值原理即可計算出[1, n]個每個像素點的值。
關鍵代碼實作及解釋:
擷取四個采樣點,及其值,然後使用類似雙線性算法計算出[x,y]的随機數值進而計算出像素值
的程式代碼如下:
// bi-line interpolation algorithm here!!!
double getcolor(int x, int y, int m, int colortype)
{
int x0 = x - (x % m);
int x1 = x0 + m;
int y0 = y - (y % m);
int y1 = y0 + m;
double x0y0 = noise(x0,y0, colortype);
double x1y0 = noise(x1,y0, colortype);
double x0y1 = noise(x0,y1, colortype);
double x1y1 = noise(x1,y1, colortype);
double xx0 =interpolate(x0, x0y0, x1, x1y0, x);
double xx1 = interpolate(x0,x0y1, x1, x1y1, x);
double n =interpolate(y0, xx0, y1, xx1, y);
return n;
}
根據兩個點計算插入值的公式代碼如下:
return (1.0 + math.cos(math.pi + (math.pi / (x1-x0)) * (x-x0))) / 2.0
* (xx1-xx0) + xx0;
對一張圖像實作随機噪聲值得出像素值計算的代碼如下:
for(int row=0; row<256; row++) {
for(int col=0; col<256; col++) {
// set random color value for each pixel
r = (int)(255.0d * getcolor(row, col, intervalpixels, 1));
g = (int)(255.0d * getcolor(row, col, intervalpixels, 2));
b = (int)(255.0d * getcolor(row, col, intervalpixels, 4));
rgbdata[index] = ((clamp(a) & 0xff) << 24) |
((clamp(r) & 0xff) << 16) |
((clamp(g) & 0xff) << 8) |
((clamp(b) & 0xff));
index++;
}
完全源代碼如下:
import java.awt.borderlayout;
import java.awt.dimension;
import java.awt.graphics;
import java.awt.graphics2d;
import java.awt.renderinghints;
import java.awt.image.bufferedimage;
import java.util.random;
import javax.swing.jcomponent;
import javax.swing.jframe;
public class randomnoiseimage extends jcomponent {
/**
*
*/
private static final long serialversionuid = -2236160343614397287l;
private bufferedimage image = null;
private double[] blue_random;
private double[] red_random;
private double[] green_random;
private int intervalpixels = 40; // default
public randomnoiseimage() {
super();
this.setopaque(false);
protected void paintcomponent(graphics g) {
graphics2d g2 = (graphics2d)g;
g2.setrenderinghint(renderinghints.key_antialiasing, renderinghints.value_antialias_on);
g2.drawimage(getimage(), 5, 5, image.getwidth(), image.getheight(), null);
private bufferedimage getimage() {
if(image == null) {
image = new bufferedimage(256, 256, bufferedimage.type_int_argb);
int[] rgbdata = new int[256*256];
generatenoiseimage(rgbdata);
setrgb(image, 0, 0, 256, 256, rgbdata);
}
return image;
private void generatenoiseimage(int[] rgbdata) {
int index = 0;
int a = 255;
int r = 0;
int g = 0;
int b = 0;
int sum = 256 * 256;
blue_random = new double[sum];
red_random = new double[sum];
green_random = new double[sum];
random random = new random();
for(int i=0; i< sum; i++) {
blue_random[i] = random.nextdouble();
red_random[i] = random.nextdouble();
green_random[i] = random.nextdouble();
for(int row=0; row<256; row++) {
for(int col=0; col<256; col++) {
// set random color value for each pixel
r = (int)(255.0d * getcolor(row, col, intervalpixels, 1));
g = (int)(255.0d * getcolor(row, col, intervalpixels, 2));
b = (int)(255.0d * getcolor(row, col, intervalpixels, 4));
rgbdata[index] = ((clamp(a) & 0xff) << 24) |
((clamp(r) & 0xff) << 16) |
((clamp(g) & 0xff) << 8) |
((clamp(b) & 0xff));
index++;
}
private int clamp(int rgb) {
if(rgb > 255)
return 255;
if(rgb < 0)
return 0;
return rgb;
// bi-line interpolation algorithm here!!!
int x0 = x - (x % m);
int x1 = x0 + m;
int y0 = y - (y % m);
int y1 = y0 + m;
double x0y0 = noise(x0, y0, colortype);
double x1y0 = noise(x1, y0, colortype);
double x0y1 = noise(x0, y1, colortype);
double x1y1 = noise(x1, y1, colortype);
double xx0 = interpolate(x0, x0y0, x1, x1y0, x);
double xx1 = interpolate(x0, x0y1, x1, x1y1, x);
double n = interpolate(y0, xx0, y1, xx1, y);
// algorithm selection here !!!
private double interpolate(double x0, double xx0, double x1, double xx1, double x) {
return (1.0 + math.cos(math.pi +
(math.pi / (x1-x0)) * (x-x0))) / 2.0 * (xx1-xx0) + xx0;
double noise(int x, int y, int colortype)
if(colortype == 1) {
if (x < 256 && y < 256)
return red_random[y * 256 + x];
else
return 0.0;
} else if(colortype == 2) {
return green_random[y * 256 + x];
} else {
return blue_random[y * 256 + x];
public void setrgb( bufferedimage image, int x, int y, int width, int height, int[] pixels ) {
int type = image.gettype();
if ( type == bufferedimage.type_int_argb || type == bufferedimage.type_int_rgb )
image.getraster().setdataelements( x, y, width, height, pixels );
else
image.setrgb( x, y, width, height, pixels, 0, width );
public static void main(string[] args) {
jframe frame = new jframe("noise art panel");
frame.setdefaultcloseoperation(jframe.exit_on_close);
frame.getcontentpane().setlayout(new borderlayout());
// display the window.
frame.getcontentpane().add(new randomnoiseimage(), borderlayout.center);
frame.setpreferredsize(new dimension(280,305));
frame.pack();
frame.setvisible(true);