java - Java 中的离散小波变换在图像中创建白点

Question

在我的 Java 程序中，图像被加载到程序中，然后使用离散小波变换进行变换，所得系数用作输出图像的图片数据。

该过程适用于自然图像:/image/q7qVp.jpg

但是，如果我变换例如卡通图像，则近似子带中的暗边缘会出现白点:/image/aRtIR.jpg

这里是forwardDWT的代码:

private int[][] transformPixels(int[][] pixels, int widthHeight) {
    double[][] temp_bank = new double[widthHeight][widthHeight];
    double a1 = -1.586134342;
    double a2 = -0.05298011854;
    double a3 = 0.8829110762;
    double a4 = 0.4435068522;

    // Scale coeff:
    double k1 = 0.81289306611596146; // 1/1.230174104914
    double k2 = 0.61508705245700002;// 1.230174104914/2
    for (int i = 0; i < 2; i++) {
        for (int col = 0; col < widthHeight; col++) {
            // Predict 1
            for (int row = 1; row < widthHeight - 1; row += 2) {
                pixels[row][col] += a1 * (pixels[row - 1][col] + pixels[row + 1][col]);
            }
            pixels[widthHeight - 1][col] += 2 * a1 * pixels[widthHeight - 2][col];

            // Update 1
            for (int row = 2; row < widthHeight; row += 2) {
                pixels[row][col] += a2 * (pixels[row - 1][col] + pixels[row + 1][col]);
            }
            pixels[0][col] += 2 * a2 * pixels[1][col];

            // Predict 2
            for (int row = 1; row < widthHeight - 1; row += 2) {
                pixels[row][col] += a3 * (pixels[row - 1][col] + pixels[row + 1][col]);
            }
            pixels[widthHeight - 1][col] += 2 * a3 * pixels[widthHeight - 2][col];

            // Update 2
            for (int row = 2; row < widthHeight; row += 2) {
                pixels[row][col] += a4 * (pixels[row - 1][col] + pixels[row + 1][col]);
            }
            pixels[0][col] += 2 * a4 * pixels[1][col];
        }

        for (int row = 0; row < widthHeight; row++) {
            for (int col = 0; col < widthHeight; col++) {
                if (row % 2 == 0)
                    temp_bank[col][row / 2] = k1 * pixels[row][col];
                else
                    temp_bank[col][row / 2 + widthHeight / 2] = k2 * pixels[row][col];

            }
        }

        for (int row = 0; row < widthHeight; row++) {
            for (int col = 0; col < widthHeight; col++) {
                pixels[row][col] = (int) temp_bank[row][col];
            }
        }
    }
    return pixels;
}

这是使用提升方案实现的 CDF9/7 fitlerbanks 的 DWT，类似于 JPEG2000 中的 DWT。

该算法有两个局限性:

1. 只能处理灰度数据
2. 图像的宽度和高度必须相同，并且是 2^n 的乘积，例如256x256、512x512 等

因为也可能是灰度值计算错误，这里是加载图像、启动转换、将 RGB 值转换为灰度以及转换回 RGB 的其他代码:

public BufferedImage openImage() throws InvalidWidthHeightException {
    try {
        int returnVal = fc.showOpenDialog(panel);
        if (returnVal == JFileChooser.APPROVE_OPTION) {
            File file = fc.getSelectedFile();
            BufferedImage temp = ImageIO.read(file);
            if (temp == null)
                return null;
            int checkInt = temp.getWidth();
            boolean check = (checkInt & (checkInt - 1)) == 0;
            if (checkInt != temp.getHeight() & !check)
                throw new InvalidWidthHeightException();
            int widthandHeight = temp.getWidth();
            image = new BufferedImage(widthandHeight, widthandHeight, BufferedImage.TYPE_BYTE_GRAY);
            Graphics g = image.getGraphics();
            g.drawImage(temp, 0, 0, null);
            g.dispose();

            return image;

        }
    } catch (IOException e) {
        System.out.println("Failed to load image!");
    }
    return null;

}

public void transform(int count) {
    int[][] pixels = getGrayValues(image);
    int transformedPixels[][];
    int width = pixels.length;
    transformedPixels = transformPixels(pixels, width);
    width/=2;

    for (int i = 1; i < count + 1; i++) {
        transformedPixels = transformPixels(transformedPixels, width);
        width/=2;
    }
    width = pixels.length;
    transformedImage = new BufferedImage(width, width, BufferedImage.TYPE_BYTE_GRAY);
    for (int x = 0; x < width; x++) {
        for (int y = 0; y < width; y++) {
            transformedImage.setRGB(x, y, tranformToRGB(transformedPixels[x][y]));
        }
    }

}

private int tranformToRGB(double d) {
    int value = (int) d;
    if (d < 0)
        d = 0;
    if (d > 255)
        d = 255;
    return 0xffffffff << 24 | value << 16 | value << 8 | value;
}

private int[][] getGrayValues(BufferedImage image2) {
    int[][] res = new int[image.getHeight()][image.getWidth()];
    int r, g, b;
    for (int i = 0; i < image.getWidth(); i++) {
        for (int j = 0; j < image.getHeight(); j++) {
            int value = image2.getRGB(i, j);
            r = (value >> 16) & 0xFF;
            g = (value >> 8) & 0xFF;
            b = (value & 0xFF);
            res[i][j] = (r + g + b) / 3;
        }
    }
    return res;
}

注意:因为图像的宽度和高度预计是相同的，所以有时我也使用宽度来表示高度。

编辑:根据@stuhlo的建议，我添加了对forwardDWT中近似子带值的检查:

for (int row = 0; row < widthHeight; row++) {
            for (int col = 0; col < widthHeight; col++) {
                if (row % 2 == 0) {
                    double value = k1 * pixels[row][col];
                    if (value > 255)
                        value = 255;
                    if (value < 0)
                        value = 0;
                    temp_bank[col][row / 2] = value;
                } else {
                    temp_bank[col][row / 2 + widthHeight / 2] = k2 * pixels[row][col];
                }
            }
        }

不幸的是，现在水平细节的子区域变成黑色。

Answer 1

您的问题是由于子带样本需要比原始图像样本更多的位数来存储而引起的。

我建议使用更大的数据类型来存储子带样本并将其标准化回 8 位值以进行显示。