平滑处理实现原理(2)

这里参考OpenCV的实现，不过应该还有优化空间，因为还没用到分离滤波器。

首先我们做一个getGaussianKernel来返回高斯滤波器的一维数组。

复制代码 代码如下:

function getGaussianKernel(__n, __sigma){
var SMALL_GAUSSIAN_SIZE = 7,
smallGaussianTab = [[1],
[0.25, 0.5, 0.25],
[0.0625, 0.25, 0.375, 0.25, 0.0625],
[0.03125, 0.109375, 0.21875, 0.28125, 0.21875, 0.109375, 0.03125]
];

var fixedKernel = __n & 2 == 1 && __n <= SMALL_GAUSSIAN_SIZE && __sigma <= 0 ? smallGaussianTab[__n >> 1] : 0;

var sigmaX = __sigma > 0 ? __sigma : ((__n - 1) * 0.5 - 1) * 0.3 + 0.8,
scale2X = -0.5 / (sigmaX * sigmaX),
sum = 0;

var i, x, t, kernel = [];

for(i = 0; i < __n; i++){
x = i - (__n - 1) * 0.5;
t = fixedKernel ? fixedKernel[i] : Math.exp(scale2X * x * x);
kernel[i] = t;
sum += t;
}

sum = 1 / sum;

for(i = __n; i--;){
kernel[i] *= sum;
}

return kernel;
};

复制代码 代码如下:

function GaussianBlur(__src, __size1, __size2, __sigma1, __sigma2, __borderType, __dst){
if(__src.type && __src.type == "CV_RGBA"){
var height = __src.row,
width = __src.col,
dst = __dst || new Mat(height, width, CV_RGBA),
dstData = dst.data;
var sigma1 = __sigma1 || 0,
sigma2 = __sigma2 || __sigma1;
var size1 = __size1 || Math.round(sigma1 * 6 + 1) | 1,
size2 = __size2 || Math.round(sigma2 * 6 + 1) | 1,
size = size1 * size2;
if(size1 % 2 !== 1 || size2 % 2 !== 1){
console.error("size必须是奇数。");
return __src;
}
var startX = Math.floor(size1 / 2),
startY = Math.floor(size2 / 2);
var withBorderMat = copyMakeBorder(__src, startY, startX, 0, 0, __borderType),
mData = withBorderMat.data,
mWidth = withBorderMat.col;

var kernel1 = getGaussianKernel(size1, sigma1),
kernel2,
kernel = new Array(size1 * size2);

if(size1 === size2 && sigma1 === sigma2)
kernel2 = kernel1;
else
kernel2 = getGaussianKernel(size2, sigma2);

var i, j, c, y, x;

for(i = kernel2.length; i--;){
for(j = kernel1.length; j--;){
kernel[i * size1 + j] = kernel2[i] * kernel1[j];
}
}

var newValue, nowX, offsetY, offsetI;

for(i = height; i--;){
offsetI = i * width;
for(j = width; j--;){
for(c = 3; c--;){
newValue = 0;
for(y = size2; y--;){
offsetY = (y + i) * mWidth * 4;
for(x = size1; x--;){
nowX = (x + j) * 4 + c;
newValue += (mData[offsetY + nowX] * kernel[y * size1 + x]);
}
}
dstData[(j + offsetI) * 4 + c] = newValue;
}
dstData[(j + offsetI) * 4 + 3] = mData[offsetY + startY * mWidth * 4 + (j + startX) * 4 + 3];
}
}

}else{
console.error("不支持的类型");
}
return dst;
}

中值平滑

中值滤波将图像的每个像素用邻域 (以当前像素为中心的正方形区域)像素的