Qt图像处理技术一:对QImage图片美颜，使用双指数滤波
一、效果图
二、demo源码地址（除了磨皮还有一些基本的滤镜）
如果你觉得有用的话，期待你的小星星
实战应用项目:
github :https://github.com/dependon/simple-image-filter //纯qt图像处理项目
三、接口代码
img参数是原始图像，返回的是处理后的图像
#include 四、原始代码（上面接口是根据原始代码修改的）
//原始代码
static void RunBEEPSVerticalHorizontal(double *data,int width,int height,double spatialDecay,double *exp_table,double *g_table)
{
int length0=height*width;
double* g= new double[length0];
int m = 0;
for (int k2 = 0;k2<height;++k2)
{
int n = k2;
for (int k1 = 0;k1<width;++k1)
{
g[n]=data[m++];
n += height;
}
}
double*p = new double[length0];
double*r = new double[length0];
memcpy(p, g, sizeof(double) * length0);
memcpy(r, g, sizeof(double) * length0);
for (int k1 = 0;k1<width; ++k1)
{
int startIndex=k1 * height;
double mu = 0.0;
for (int k=startIndex+1,K =startIndex+height;k<K;++k)
{
int div0=fabs(p[k]-p[k-1]);
mu =exp_table[div0];
p[k] = p[k - 1] * mu + p[k] * (1.0 - mu);//文献中的公式1，这里做了一下修改，效果影响不大
}
for (int k =startIndex+height-2;startIndex <= k;--k)
{
int div0=fabs(r[k]-r[k+1]);
mu =exp_table[div0];
r[k] = r[k+1] * mu + r[k] * (1.0-mu) ;//文献公式3
}
}
double rho0=1.0/(2-spatialDecay);
for (int k = 0;k <length0;++k)
{
r[k]= (r[k]+p[k])*rho0-g_table[(int)g[k]];
}
m = 0;
for (int k1=0;k1<width;++k1)
{
int n = k1;
for (int k2 =0;k2<height;++k2)
{
data[n] = r[m++];
n += width;
}
}
memcpy(p,data, sizeof(double) * length0);
memcpy(r,data, sizeof(double) * length0);
for (int k2 = 0; k2<height;++k2)
{
int startIndex=k2 * width;
double mu = 0.0;
for (int k=startIndex+1, K=startIndex+width;k<K;++k)
{
int div0=fabs(p[k]-p[k-1]);
mu =exp_table[div0];
p[k] = p[k - 1] * mu + p[k] * (1.0 - mu);
}
for (int k=startIndex+width-2;startIndex<=k;--k)
{
int div0=fabs(r[k]-r[k+1]);
mu =exp_table[div0];
r[k] = r[k + 1] * mu + r[k] * (1.0 - mu) ;
}
}
double init_gain_mu=spatialDecay/(2-spatialDecay);
for (int k = 0; k <length0; k++)
{
data[k]=(p[k]+r[k])*rho0-data[k]*init_gain_mu;//文献中的公式5
}
delete[] p;
delete[] r;
delete[] g;
}
四、公式原理
The first recursion is progressive. Letting x[k] be the current sample of an input sequence x at location k ∈ Z,were cursively compute the elements of an auxiliary sequence ϕ as：
where：
The second recursion is regressive and very similar to thefirst one, except for a reversal of the order in which the indices are traversed. We recursively compute a second auxiliary sequence φ as：
where：
We complete our algorithm by merging the resulting progressive sequence ϕ and regressive sequence φ to produce the samples of the output sequence y as：
在这里我们主要使用到了上面的公式1和公式3，并在关键代码使用里面有所标注。
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