Lanczos算法放大和缩小图片（Java）

由于最近的Java作业要求将图片放大缩小，主要就是选用一种插值算法，如最邻近插值、双线性二次插值、双线性三次插值，Lanczos插值算法等。本文主要讲如何用Java实现Lanczos算法，详细原理见Lanczos Resampling

Lanczos插值的原理其实就是在原图像中取一个小窗口，通过计算权重映射到新的图像中的一个像素点中。

权重计算公式如下:

weight compute1

等同于:

weight compute1

可以看出，对于一维的数组，这个窗口其实就是−a ≤ x ≤ a

然后根据如下公式取加权平均值:

weight average

扩展到二维，权重计算为:

2D weight compute

2D weight average

可以看到窗口的大小就是2a * 2a。

a

当a = 2时，该算法适合图像缩小，当a = 3时，该算法适合图像放大。

接下来是我用Java实现Lanczos插值(只支持了jpg、png和bmp格式的图片):

public BufferedImage imageScale(String pathName, float widthScale, float heightScale) throws IOException {
    if (!pathName.endsWith("png") && !pathName.endsWith("jpg") && !pathName.endsWith("bmp")) {
        throw new RuntimeException("Wrrong File!");
    }
    File file = new File(pathName);
    BufferedImage bufferedImage = ImageIO.read(file);
        
    widthScale = 1/widthScale;
    heightScale = 1/heightScale;
    lanczosSize = widthScale > 1 ? 3 : 2;
    int srcW = bufferedImage.getWidth();
    int srcH = bufferedImage.getHeight();
    int destW = (int)(bufferedImage.getWidth() / widthScale);
    int destH = (int)(bufferedImage.getHeight() / heightScale);
    
    int[] inPixels = bufferedImage.getRGB(0, 0, srcW, srcH, null, 0, srcW);
    int[] outPixels = new int[destW * destH];

    for (int col = 0; col < destW; col++) {
            
            
        double x = col * widthScale; 
        double fx = (double)Math.floor(col * widthScale);
        for (int row = 0; row < destH; row ++) {
                
            double y = row * heightScale;
            double fy = (double)Math.floor(y);
            double[] argb = {0, 0, 0, 0};
            int[] pargb = {0, 0, 0, 0};
            double totalWeight = 0;
            //  计算窗口的权重 
            for (int subrow = (int)(fy - lanczosSize + 1); subrow <= fy + lanczosSize; subrow++) {
                if (subrow < 0 || subrow >= srcH) 
                    continue;
                    
                for (int subcol = (int)(fx - lanczosSize + 1); subcol <= fx + lanczosSize; subcol++) {
                    if (subcol < 0 || subcol >= srcW)  
                        continue;
                        
                    double weight = getLanczosFactor(x - subcol) * getLanczosFactor(y - subrow);
                    
                    if (weight > 0) { 
                        int index = (subrow * srcW + subcol); 
                        for (int i = 0; i < 4; i++)
                            pargb[i] = (inPixels[index] >> 24 - 8 * i) & 0xff;
                        totalWeight += weight; 
                        for (int i = 0; i < 4; i++)
                            argb[i] += weight * pargb[i];
                    }
                }
            }
            for (int i = 0; i < 4; i++)
                pargb[i] = (int)(argb[i] / totalWeight);
            outPixels[row * destW + col] = (clamp(pargb[0]) << 24) |
                        (clamp(pargb[1]) << 16) |
                        (clamp(pargb[2]) << 8) |
                        clamp(pargb[3]);
        }
    }
        
    BufferedImage bufImg = new BufferedImage(destW, destH,
            pathName.endsWith("png") ? BufferedImage.TYPE_INT_ARGB : BufferedImage.TYPE_INT_RGB);
        
    bufImg.setRGB(0, 0, destW, destH, outPixels, 0, destW);
    return bufImg;
}
private int clamp(int v)  
{  
    return v > 255 ? 255 : (v < 0 ? 0 : v);  
}  
  
private double getLanczosFactor(double x) {  
    if (x >= lanczosSize)  
        return 0;   
    if (Math.abs(x) < 1e-16)  
        return 1;  
    x *= Math.PI; 
    return Math.sin(x) * Math.sin(x/lanczosSize) / (x*x);  
}  

我们来看一下处理效果:

source

原图

zoom out

放大

zoom in

缩小

源码可见我的github