图像的放大与缩小(2)——双线性插值放大与均值缩小
2016-07-14 18:15
621 查看
概述
基于上一节“等距采样法”实现图片放大与缩小的缺点。要对其进行改进,对图像的缩小则可以用“局部均值法”,对于图像的放大则可以用“双线性插值法”。效果如下:
2048*1536缩小为100*80时的效果
100*80放大到600*400的效果
局部均值法缩小图像
(1)计算采样间隔
设原图的大小为W*H,将其放大(缩小)为(k1*W)*(K2*H),则采样区间为ii=1/k1; jj=1/k2;
当k1==k2时为等比例缩小;当k1!=k2时为不等比例放大(缩小);当k1<1 && k2<1时为图片缩小,k1>1 &&
k2>1时图片放大。
(2)求出局部子块
设原图为F(x,y)(i=1,2,……W; j=1,2,……H),缩小的图像为G(x,y)(x=1,2, ……M; y=1,2,……N,其中M=W*k1,N=H*k2),则有原图像局部子块为f’(x,y) = f(ii*i, jj*j) …… f(ii*i + ii-1, jj*j)
…… ……
f(ii*i, jj*j+jj-1) …… f(ii*i + ii-1, jj*j+jj-1)
(3)求出缩小的图像
G(x, y) = f’(x,y)的均值例:
缩小后的图像
例如g11=(f11 +f12 + f21 + f22)/4
算法源代码(Java)
[java] view
plain copy
/**
* 局部均值的图像缩小
* @param img 要缩小的图像对象
* @param m 缩小后图像的宽
* @param n 缩小后图像的高
* @return 返回处理后的图像对象
*/
public static BufferedImage shrink(BufferedImage img, int m, int n) {
float k1 = (float)m/img.getWidth();
float k2 = (float)n/img.getHeight();
return shrink(img, k1, k2);
}
/**
* 局部均值的图像缩小
* @param img 要缩小的图像对象
* @param k1 要缩小的列比列
* @param k2 要缩小的行比列
* @return 返回处理后的图像对象
*/
public static BufferedImage shrink(BufferedImage img, float k1, float k2) {
if(k1 >1 || k2>1) {//如果k1 >1 || k2>1则是图片放大,不是缩小
System.err.println("this is shrink image funcation, please set k1<=1 and k2<=1!");
return null;
}
float ii = 1/k1; //采样的行间距
float jj = 1/k2; //采样的列间距
int dd = (int)(ii*jj);
//int m=0 , n=0;
int imgType = img.getType();
int w = img.getWidth();
int h = img.getHeight();
int m = (int) (k1*w);
int n = (int) (k2*h);
int[] pix = new int[w*h];
pix = img.getRGB(0, 0, w, h, pix, 0, w);
System.out.println(w + " * " + h);
System.out.println(m + " * " + n);
int[] newpix = new int[m*n];
for(int j=0; j<n; j++) {
for(int i=0; i<m; i++) {
int r = 0, g=0, b=0;
ColorModel cm = ColorModel.getRGBdefault();
for(int k=0; k<(int)jj; k++) {
for(int l=0; l<(int)ii; l++) {
r = r + cm.getRed(pix[(int)(jj*j+k)*w + (int)(ii*i+l)]);
g = g + cm.getGreen(pix[(int)(jj*j+k)*w + (int)(ii*i+l)]);
b = b + cm.getBlue(pix[(int)(jj*j+k)*w + (int)(ii*i+l)]);
}
}
r = r/dd;
g = g/dd;
b = b/dd;
newpix[j*m + i] = 255<<24 | r<<16 | g<<8 | b;
//255<<24 | r<<16 | g<<8 | b 这个公式解释一下,颜色的RGB在内存中是
//以二进制的形式保存的,从右到左1-8位表示blue,9-16表示green,17-24表示red
//所以"<<24" "<<16" "<<8"分别表示左移24,16,8位
//newpix[j*m + i] = new Color(r,g,b).getRGB();
}
}
BufferedImage imgOut = new BufferedImage( m, n, imgType);
imgOut.setRGB(0, 0, m, n, newpix, 0, m);
return imgOut;
}
[java] view
plain copy
/**
* 局部均值的图像缩小
* @param img 要缩小的图像对象
* @param m 缩小后图像的宽
* @param n 缩小后图像的高
* @return 返回处理后的图像对象
*/
public static BufferedImage shrink(BufferedImage img, int m, int n) {
float k1 = (float)m/img.getWidth();
float k2 = (float)n/img.getHeight();
return shrink(img, k1, k2);
}
/**
* 局部均值的图像缩小
* @param img 要缩小的图像对象
* @param k1 要缩小的列比列
* @param k2 要缩小的行比列
* @return 返回处理后的图像对象
*/
public static BufferedImage shrink(BufferedImage img, float k1, float k2) {
if(k1 >1 || k2>1) {//如果k1 >1 || k2>1则是图片放大,不是缩小
System.err.println("this is shrink image funcation, please set k1<=1 and k2<=1!");
return null;
}
float ii = 1/k1; //采样的行间距
float jj = 1/k2; //采样的列间距
int dd = (int)(ii*jj);
//int m=0 , n=0;
int imgType = img.getType();
int w = img.getWidth();
int h = img.getHeight();
int m = (int) (k1*w);
int n = (int) (k2*h);
int[] pix = new int[w*h];
pix = img.getRGB(0, 0, w, h, pix, 0, w);
System.out.println(w + " * " + h);
System.out.println(m + " * " + n);
int[] newpix = new int[m*n];
for(int j=0; j<n; j++) {
for(int i=0; i<m; i++) {
int r = 0, g=0, b=0;
ColorModel cm = ColorModel.getRGBdefault();
for(int k=0; k<(int)jj; k++) {
for(int l=0; l<(int)ii; l++) {
r = r + cm.getRed(pix[(int)(jj*j+k)*w + (int)(ii*i+l)]);
g = g + cm.getGreen(pix[(int)(jj*j+k)*w + (int)(ii*i+l)]);
b = b + cm.getBlue(pix[(int)(jj*j+k)*w + (int)(ii*i+l)]);
}
}
r = r/dd;
g = g/dd;
b = b/dd;
newpix[j*m + i] = 255<<24 | r<<16 | g<<8 | b;
//255<<24 | r<<16 | g<<8 | b 这个公式解释一下,颜色的RGB在内存中是
//以二进制的形式保存的,从右到左1-8位表示blue,9-16表示green,17-24表示red
//所以"<<24" "<<16" "<<8"分别表示左移24,16,8位
//newpix[j*m + i] = new Color(r,g,b).getRGB();
}
}
BufferedImage imgOut = new BufferedImage( m, n, imgType);
imgOut.setRGB(0, 0, m, n, newpix, 0, m);
return imgOut;
}
双线性差值法放图像
子块四个顶点的坐标分别设为(0,0)、(1,0)、(0,1)、(1,1),对应的带处理的像素的坐标(c1,c2),0<c1<1, 0<y<1.则f(x,y)由上到下得到f(x,0) = f(0,0) + c1*(f(1,0)-f(0,0))
f(x,1) = f(0,1) + c1*(f(1,1)-f(0,1))
f(x,y) = f(x,0) + c2*f(f(x,1)-f(x,0))
例,原图的像素矩阵如下。
将其放大成2.5*1.2倍,双线性插值发,填充顶点如下:
(1)
(2)
1 2 3 4 5 6 7 7
2 3 4 5 7 8 8 8
3 4 5 6 7 8 9 9
3 4 5 6 7 8 9 9
(3)
算法源代码(java)
[java] view
plain copy
/**
* 双线性插值法图像的放大
* @param img 要缩小的图像对象
* @param k1 要缩小的列比列
* @param k2 要缩小的行比列
* @return 返回处理后的图像对象
*/
public static BufferedImage amplify(BufferedImage img, float k1, float k2) {
if(k1 <1 || k2<1) {//如果k1 <1 || k2<1则是图片缩小,不是放大
System.err.println("this is shrink image funcation, please set k1<=1 and k2<=1!");
return null;
}
float ii = 1/k1; //采样的行间距
float jj = (1/k2); //采样的列间距
int dd = (int)(ii*jj);
//int m=0 , n=0;
int imgType = img.getType();
int w = img.getWidth(); //原图片的宽
int h = img.getHeight(); //原图片的宽
int m = Math.round(k1*w); //放大后图片的宽
int n = Math.round(k2*h); //放大后图片的宽
int[] pix = new int[w*h];
pix = img.getRGB(0, 0, w, h, pix, 0, w);
/*System.out.println(w + " * " + h);
System.out.println(m + " * " + n);*/
int[] newpix = new int[m*n];
for(int j=0; j<h-1; j++){
for(int i=0; i<w-1; i++) {
int x0 = Math.round(i*k1);
int y0 = Math.round(j*k2);
int x1, y1;
if(i == w-2) {
x1 = m-1;
} else {
x1 = Math.round((i+1)*k1);
}
if(j == h-2) {
y1 = n-1;
} else {
y1 = Math.round((j+1)*k2);
}
int d1 = x1 - x0;
int d2 = y1 - y0;
if(0 == newpix[y0*m + x0]) {
newpix[y0*m + x0] = pix[j*w+i];
}
if(0 == newpix[y0*m + x1]) {
if(i == w-2) {
newpix[y0*m + x1] = pix[j*w+w-1];
} else {
newpix[y0*m + x1] = pix[j*w+i+1];
}
}
if(0 == newpix[y1*m + x0]){
if(j == h-2) {
newpix[y1*m + x0] = pix[(h-1)*w+i];
} else {
newpix[y1*m + x0] = pix[(j+1)*w+i];
}
}
if(0 == newpix[y1*m + x1]) {
if(i==w-2 && j==h-2) {
newpix[y1*m + x1] = pix[(h-1)*w+w-1];
} else {
newpix[y1*m + x1] = pix[(j+1)*w+i+1];
}
}
int r, g, b;
float c;
ColorModel cm = ColorModel.getRGBdefault();
for(int l=0; l<d2; l++) {
for(int k=0; k<d1; k++) {
if(0 == l) {
//f(x,0) = f(0,0) + c1*(f(1,0)-f(0,0))
if(j<h-1 && newpix[y0*m + x0 + k] == 0) {
c = (float)k/d1;
r = cm.getRed(newpix[y0*m + x0]) + (int)(c*(cm.getRed(newpix[y0*m + x1]) - cm.getRed(newpix[y0*m + x0])));//newpix[(y0+l)*m + k]
g = cm.getGreen(newpix[y0*m + x0]) + (int)(c*(cm.getGreen(newpix[y0*m + x1]) - cm.getGreen(newpix[y0*m + x0])));
b = cm.getBlue(newpix[y0*m + x0]) + (int)(c*(cm.getBlue(newpix[y0*m + x1]) - cm.getBlue(newpix[y0*m + x0])));
newpix[y0*m + x0 + k] = new Color(r,g,b).getRGB();
}
if(j+1<h && newpix[y1*m + x0 + k] == 0) {
c = (float)k/d1;
r = cm.getRed(newpix[y1*m + x0]) + (int)(c*(cm.getRed(newpix[y1*m + x1]) - cm.getRed(newpix[y1*m + x0])));
g = cm.getGreen(newpix[y1*m + x0]) + (int)(c*(cm.getGreen(newpix[y1*m + x1]) - cm.getGreen(newpix[y1*m + x0])));
b = cm.getBlue(newpix[y1*m + x0]) + (int)(c*(cm.getBlue(newpix[y1*m + x1]) - cm.getBlue(newpix[y1*m + x0])));
newpix[y1*m + x0 + k] = new Color(r,g,b).getRGB();
}
//System.out.println(c);
} else {
//f(x,y) = f(x,0) + c2*f(f(x,1)-f(x,0))
c = (float)l/d2;
r = cm.getRed(newpix[y0*m + x0+k]) + (int)(c*(cm.getRed(newpix[y1*m + x0+k]) - cm.getRed(newpix[y0*m + x0+k])));
g = cm.getGreen(newpix[y0*m + x0+k]) + (int)(c*(cm.getGreen(newpix[y1*m + x0+k]) - cm.getGreen(newpix[y0*m + x0+k])));
b = cm.getBlue(newpix[y0*m + x0+k]) + (int)(c*(cm.getBlue(newpix[y1*m + x0+k]) - cm.getBlue(newpix[y0*m + x0+k])));
newpix[(y0+l)*m + x0 + k] = new Color(r,g,b).getRGB();
//System.out.println((int)(c*(cm.getRed(newpix[y1*m + x0+k]) - cm.getRed(newpix[y0*m + x0+k]))));
}
}
if(i==w-2 || l==d2-1) { //最后一列的计算
//f(1,y) = f(1,0) + c2*f(f(1,1)-f(1,0))
c = (float)l/d2;
r = cm.getRed(newpix[y0*m + x1]) + (int)(c*(cm.getRed(newpix[y1*m + x1]) - cm.getRed(newpix[y0*m + x1])));
g = cm.getGreen(newpix[y0*m + x1]) + (int)(c*(cm.getGreen(newpix[y1*m + x1]) - cm.getGreen(newpix[y0*m + x1])));
b = cm.getBlue(newpix[y0*m + x1]) + (int)(c*(cm.getBlue(newpix[y1*m + x1]) - cm.getBlue(newpix[y0*m + x1])));
newpix[(y0+l)*m + x1] = new Color(r,g,b).getRGB();
}
}
}
}
/*
for(int j=0; j<50; j++){
for(int i=0; i<50; i++) {
System.out.print(new Color(newpix[j*m + i]).getRed() + "\t");
}
System.out.println();
}
*/
BufferedImage imgOut = new BufferedImage( m, n, imgType);
imgOut.setRGB(0, 0, m, n, newpix, 0, m);
return imgOut;
}
相关文章推荐
- 相关技术
- 6.Note the following points describing various utilities in Oracle Database 11g:
- CV | 智能缩放:浅谈Seam Carving算法 (1)
- ButterKnife使用详解
- 指针与内存的分配
- MySql 修改字符集
- MySQL 索引及查询优化
- 计算机网络7层结构归纳总结
- 图像的放大与缩小(1)——等距采样法
- 整数数组的遍历
- swap分区
- android Window WindowManager 整理
- 连续区间最值问题
- node.js 下依赖Express 实现post 4种方式提交参数
- Eclipse之SVN插件离线安装(Mars.2 Release (4.5.2))
- 团体程序设计天梯赛-练习集L1-017. 到底有多二
- 团体程序设计天梯赛-练习集L1-016. 查验身份证
- 团体程序设计天梯赛-练习集L1-015. 跟奥巴马一起画方块
- 团体程序设计天梯赛-练习集L1-014. 简单题
- Python最好用的模板引擎Jinja