二值化是去噪的一种常见方法，最近做了一个应用，需要从纷繁的背景中获取想要的内容。因为在这个应用中背景和前景一般差距较大，所以很自然地想到用大津算法。

关于大津算法，具体内容见 维基百科：大津算法，以下是java实现：

public int Otsu(byte[] sourceData) {
  // STEP 1 计算灰度直方图
  int[] pixelCount = new int[256];
  for (int i = 0; i < sourceData.length; i++)
   pixelCount[sourceData[i] & 0xff]++;
  // STEP2 计算灰度值总和，用来计算平均灰度值
  long graySum = 0;
  for (int i = 0; i < pixelCount.length; i++)
   graySum += pixelCount[i] * i;

  // STEP 3 尝试用每一个灰度[0-255]来区分，看看哪个区分效果最好。
  int backgroundCount = 0, foregroundCount = 0, threshold = 0;
  double backgroundGraySum = 0, foregroundGraySum = 0;
  double maxDiff = 0;
  for (int i = 0; i < 255; i++) {
   backgroundCount += pixelCount[i];
   if (backgroundCount == 0)
    continue;
   foregroundCount = sourceData.length - backgroundCount;
   if (foregroundCount == 0)
    continue;
   backgroundGraySum += i * pixelCount[i];
   foregroundGraySum = graySum - backgroundGraySum;
   double backgroundMeanGray = backgroundGraySum / backgroundCount;
   double foregroundMeanGray = foregroundGraySum / foregroundCount;
   double diff = foregroundMeanGray - backgroundMeanGray;
   diff *= diff * backgroundCount * foregroundCount;
   if (diff > maxDiff) {
    threshold = i;
    maxDiff = diff;
   }
  }
  return threshold;
 }