效果
先上效果图 
原理
高斯模糊的原理中，它是根据高斯曲线调节像素色值，它是有选择地模糊图像。说得直白一点，就是高斯模糊能够把某一点周围的像素色值按高斯曲线统计起来，采用数学上加权平均的计算方法得到这条曲线的色值，最后能够留下人物的轮廓，即曲线. 更加具体的解释请移步到 阮一峰的高斯模糊算法.
本篇blog主要讨论js的高斯函数及其应用.
采用了两种分别不同的函数来处里像素位点, 第一种是直接使用二种循环, 时间复杂度为O(xy(2r)^2). 另一种是采用一维公式分别对x, y进行循环处理, 时间复杂度为O(2xy(2r)).
代码
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<title>Document</title>
</head>
<body>
<img src="林徽因.jpg" id="imgSource">
<canvas id="canvas"></canvas>
<script>
window.onload = function(){
var img = document.getElementById("imgSource"),
canvas = document.getElementById('canvas'),
width = img.width,
height = img.height;
canvas.width = width;
canvas.height = height;
var context = canvas.getContext("2d");
context.drawImage(img, 0, 0);
var canvasData = context.getImageData(0, 0, canvas.width, canvas.height);
var tempData = gaussBlur1(canvasData, 20);
context.putImageData(tempData,0,0);
}
/**
* 此函数为二重循环
*/
function gaussBlur(imgData, radius, sigma) {
var pixes = imgData.data,
width = imgData.width,
height = imgData.height;
radius = radius || 5;
sigma = sigma || radius / 3;
var gaussEdge = radius * 2 + 1; // 高斯矩阵的边长
var gaussMatrix = [],
gaussSum = 0,
a = 1 / (2 * sigma * sigma * Math.PI),
b = -a * Math.PI;
for (var i=-radius; i<=radius; i++) {
for (var j=-radius; j<=radius; j++) {
var gxy = a * Math.exp((i * i + j * j) * b);
gaussMatrix.push(gxy);
gaussSum += gxy; // 得到高斯矩阵的和，用来归一化
}
}
var gaussNum = (radius + 1) * (radius + 1);
for (var i=0; i<gaussNum; i++) {
gaussMatrix[i] = gaussMatrix[i] / gaussSum; // 除gaussSum是归一化
}
//console.log(gaussMatrix);
// 循环计算整个图像每个像素高斯处理之后的值
for (var x=0; x<width;x++) {
for (var y=0; y<height; y++) {
var r = 0,
g = 0,
b = 0;
//console.log(1);
// 计算每个点的高斯处理之后的值
for (var i=-radius; i<=radius; i++) {
// 处理边缘
var m = handleEdge(i, x, width);
for (var j=-radius; j<=radius; j++) {
// 处理边缘
var mm = handleEdge(j, y, height);
var currentPixId = (mm * width + m) * 4;
var jj = j + radius;
var ii = i + radius;
r += pixes[currentPixId] * gaussMatrix[jj * gaussEdge + ii];
g += pixes[currentPixId + 1] * gaussMatrix[jj * gaussEdge + ii];
b += pixes[currentPixId + 2] * gaussMatrix[jj * gaussEdge + ii];
}
}
var pixId = (y * width + x) * 4;
pixes[pixId] = ~~r;
pixes[pixId + 1] = ~~g;
pixes[pixId + 2] = ~~b;
}
}
imgData.data = pixes;
return imgData;
}
function handleEdge(i, x, w) {
var m = x + i;
if (m < 0) {
m = -m;
} else if (m >= w) {
m = w + i - x;
}
return m;
}
/**
* 此函数为分别循环
*/
function gaussBlur1(imgData,radius, sigma) {
var pixes = imgData.data;
var width = imgData.width;
var height = imgData.height;
var gaussMatrix = [],
gaussSum = 0,
x, y,
r, g, b, a,
i, j, k, len;
radius = Math.floor(radius) || 3;
sigma = sigma || radius / 3;
a = 1 / (Math.sqrt(2 * Math.PI) * sigma);
b = -1 / (2 * sigma * sigma);
//生成高斯矩阵
for (i = 0, x = -radius; x <= radius; x++, i++){
g = a * Math.exp(b * x * x);
gaussMatrix[i] = g;
gaussSum += g;
}
//归一化, 保证高斯矩阵的值在[0,1]之间
for (i = 0, len = gaussMatrix.length; i < len; i++) {
gaussMatrix[i] /= gaussSum;
}
//x 方向一维高斯运算
for (y = 0; y < height; y++) {
for (x = 0; x < width; x++) {
r = g = b = a = 0;
gaussSum = 0;
for(j = -radius; j <= radius; j++){
k = x + j;
if(k >= 0 && k < width){//确保 k 没超出 x 的范围
//r,g,b,a 四个一组
i = (y * width + k) * 4;
r += pixes[i] * gaussMatrix[j + radius];
g += pixes[i + 1] * gaussMatrix[j + radius];
b += pixes[i + 2] * gaussMatrix[j + radius];
// a += pixes[i + 3] * gaussMatrix[j];
gaussSum += gaussMatrix[j + radius];
}
}
i = (y * width + x) * 4;
// 除以 gaussSum 是为了消除处于边缘的像素, 高斯运算不足的问题
// console.log(gaussSum)
pixes[i] = r / gaussSum;
pixes[i + 1] = g / gaussSum;
pixes[i + 2] = b / gaussSum;
// pixes[i + 3] = a ;
}
}
//y 方向一维高斯运算
for (x = 0; x < width; x++) {
for (y = 0; y < height; y++) {
r = g = b = a = 0;
gaussSum = 0;
for(j = -radius; j <= radius; j++){
k = y + j;
if(k >= 0 && k < height){//确保 k 没超出 y 的范围
i = (k * width + x) * 4;
r += pixes[i] * gaussMatrix[j + radius];
g += pixes[i + 1] * gaussMatrix[j + radius];
b += pixes[i + 2] * gaussMatrix[j + radius];
// a += pixes[i + 3] * gaussMatrix[j];
gaussSum += gaussMatrix[j + radius];
}
}
i = (y * width + x) * 4;
pixes[i] = r / gaussSum;
pixes[i + 1] = g / gaussSum;
pixes[i + 2] = b / gaussSum;
// pixes[i] = r ;
// pixes[i + 1] = g ;
// pixes[i + 2] = b ;
// pixes[i + 3] = a ;
}
}
//end
imgData.data = pixes;
return imgData;
}
</script>
</body>
</html>