基于直方图的图像全局二值化算法原理、实现--一维最大熵

1、描述：

该算法把信息论中熵的概念引入到图像中，通过计算阈值分割后两部分熵的和来判断阈值是否为最佳阈值。

2、算法原理

3、参考代码：

int Get1DMaxEntropyThreshold(int*
HistGram)

{
int  X, Y,Amount=0;
double* HistGramD = new double[256];
double SumIntegral, EntropyBack, EntropyFore, MaxEntropy;
int MinValue = 255, MaxValue = 0;
int Threshold = 0;

for (MinValue = 0; MinValue < 256 && HistGram[MinValue] == 0; MinValue++) ;
for (MaxValue = 255; MaxValue > MinValue && HistGram[MinValue] == 0; MaxValue--) ;
if (MaxValue == MinValue) return MaxValue;          // 图像中只有一个颜色
if (MinValue + 1 == MaxValue) return MinValue;      // 图像中只有二个颜色

for (Y = MinValue; Y <= MaxValue; Y++) Amount += HistGram[Y];        //  像素总数

for (Y = MinValue; Y <= MaxValue; Y++)   HistGramD[Y] = (double)HistGram[Y] / Amount+1e-17;

MaxEntropy = double.MinValue; ;
for (Y = MinValue + 1; Y < MaxValue; Y++)
{
SumIntegral = 0;
for (X = MinValue; X <= Y; X++) SumIntegral += HistGramD[X];
EntropyBack = 0;
for (X = MinValue; X <= Y; X++) EntropyBack += (-HistGramD[X] / SumIntegral * Math.Log(HistGramD[X] / SumIntegral));
EntropyFore = 0;
for (X = Y + 1; X <= MaxValue; X++) EntropyFore += (-HistGramD[X] / (1 - SumIntegral) * Math.Log(HistGramD[X] / (1 - SumIntegral)));
if (MaxEntropy < EntropyBack + EntropyFore)
{
Threshold = Y;
MaxEntropy = EntropyBack + EntropyFore;
}
}
return Threshold;
}