用java编写代码实现图像平滑降噪（中值滤波、均值滤波、高斯滤波）

以下是Java代码实现图像平滑降噪的三种滤波方法：

1. 中值滤波：

import java.awt.Color;
import java.awt.image.BufferedImage;

public class MedianFilter {

    public static BufferedImage filter(BufferedImage image, int filterSize) {
        int width = image.getWidth();
        int height = image.getHeight();
        BufferedImage result = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);

        for (int x = 0; x < width; x++) {
            for (int y = 0; y < height; y++) {
                int[] pixels = new int[filterSize * filterSize];
                int index = 0;
                for (int i = x - filterSize / 2; i <= x + filterSize / 2; i++) {
                    for (int j = y - filterSize / 2; j <= y + filterSize / 2; j++) {
                        if (i >= 0 && i < width && j >= 0 && j < height) {
                            Color color = new Color(image.getRGB(i, j));
                            pixels[index++] = color.getRed();
                        }
                    }
                }
                java.util.Arrays.sort(pixels);
                int median = pixels[filterSize * filterSize / 2];
                result.setRGB(x, y, new Color(median, median, median).getRGB());
            }
        }

        return result;
    }
}

2. 均值滤波：

import java.awt.Color;
import java.awt.image.BufferedImage;

public class MeanFilter {

    public static BufferedImage filter(BufferedImage image, int filterSize) {
        int width = image.getWidth();
        int height = image.getHeight();
        BufferedImage result = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);

        for (int x = 0; x < width; x++) {
            for (int y = 0; y < height; y++) {
                int sum = 0;
                int count = 0;
                for (int i = x - filterSize / 2; i <= x + filterSize / 2; i++) {
                    for (int j = y - filterSize / 2; j <= y + filterSize / 2; j++) {
                        if (i >= 0 && i < width && j >= 0 && j < height) {
                            Color color = new Color(image.getRGB(i, j));
                            sum += color.getRed();
                            count++;
                        }
                    }
                }
                int mean = sum / count;
                result.setRGB(x, y, new Color(mean, mean, mean).getRGB());
            }
        }

        return result;
    }
}

3. 高斯滤波：

import java.awt.Color;
import java.awt.image.BufferedImage;

public class GaussianFilter {

    public static BufferedImage filter(BufferedImage image, int filterSize, double sigma) {
        int width = image.getWidth();
        int height = image.getHeight();
        BufferedImage result = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);

        double[] kernel = new double[filterSize * filterSize];
        double kernelSum = 0;
        for (int i = 0; i < filterSize; i++) {
            for (int j = 0; j < filterSize; j++) {
                int x = i - filterSize / 2;
                int y = j - filterSize / 2;
                kernel[i * filterSize + j] = Math.exp(-(x * x + y * y) / (2 * sigma * sigma));
                kernelSum += kernel[i * filterSize + j];
            }
        }
        for (int i = 0; i < kernel.length; i++) {
            kernel[i] /= kernelSum;
        }

        for (int x = 0; x < width; x++) {
            for (int y = 0; y < height; y++) {
                double sum = 0;
                for (int i = x - filterSize / 2; i <= x + filterSize / 2; i++) {
                    for (int j = y - filterSize / 2; j <= y + filterSize / 2; j++) {
                        if (i >= 0 && i < width && j >= 0 && j < height) {
                            Color color = new Color(image.getRGB(i, j));
                            double weight = kernel[(i - x + filterSize / 2) * filterSize + j - y + filterSize / 2];
                            sum += weight * color.getRed();
                        }
                    }
                }
                int value = (int) Math.round(sum);
                if (value < 0) {
                    value = 0;
                }
                if (value > 255) {
                    value = 255;
                }
                result.setRGB(x, y, new Color(value, value, value).getRGB());
            }
        }

        return result;
    }
}

以上代码中，`image` 是输入的 BufferedImage 对象，`filterSize` 是滤波器的大小，`sigma` 是高斯滤波的标准差。对于中值滤波和均值滤波，`result` 是经过滤波处理后的 BufferedImage 对象；对于高斯滤波，`kernel` 是高斯卷积核，`kernelSum` 是卷积核的和，`sum` 是卷积结果的和，`value` 是最终的像素值。