JAVA语言完成对灰度图像的图像增强操作，包括图像读取，结果展示，结果储存，至少三种图像增强

好的，我可以为您提供 JAVA 语言完成对灰度图像的图像增强操作的示例代码，并包括图像读取、结果展示、结果储存等功能。以下是示例代码：

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;

public class ImageEnhancement {

    public static void main(String[] args) throws IOException {
        // 读取灰度图像
        BufferedImage img = ImageIO.read(new File("input.png"));

        // 进行图像增强操作
        BufferedImage result1 = contrastEnhancement(img); // 对比度增强
        BufferedImage result2 = histogramEqualization(img); // 直方图均衡化
        BufferedImage result3 = medianFiltering(img); // 中值滤波

        // 将结果展示出来
        displayImage(result1, "Contrast Enhancement");
        displayImage(result2, "Histogram Equalization");
        displayImage(result3, "Median Filtering");

        // 将结果储存到本地
        saveImage(result1, "result1.png");
        saveImage(result2, "result2.png");
        saveImage(result3, "result3.png");
    }

    // 对比度增强
    public static BufferedImage contrastEnhancement(BufferedImage img) {
        int width = img.getWidth();
        int height = img.getHeight();
        int max_gray = 0;
        int min_gray = 255;

        // 获取最大灰度值和最小灰度值
        for (int i = 0; i < width; i++) {
            for (int j = 0; j < height; j++) {
                Color c = new Color(img.getRGB(i, j));
                int gray = (c.getRed() + c.getGreen() + c.getBlue()) / 3;
                if (gray > max_gray) {
                    max_gray = gray;
                }
                if (gray < min_gray) {
                    min_gray = gray;
                }
            }
        }

        // 对每个像素进行对比度增强
        BufferedImage result = new BufferedImage(width, height, BufferedImage.TYPE_BYTE_GRAY);
        for (int i = 0; i < width; i++) {
            for (int j = 0; j < height; j++) {
                Color c = new Color(img.getRGB(i, j));
                int gray = (c.getRed() + c.getGreen() + c.getBlue()) / 3;
                int new_gray = (gray - min_gray) * 255 / (max_gray - min_gray);
                new_gray = Math.max(0, Math.min(255, new_gray)); // 确保灰度值在 0-255 范围内
                result.setRGB(i, j, new Color(new_gray, new_gray, new_gray).getRGB());
            }
        }
        return result;
    }

    // 直方图均衡化
    public static BufferedImage histogramEqualization(BufferedImage img) {
        int width = img.getWidth();
        int height = img.getHeight();
        int[] histogram = new int[256];

        // 计算灰度直方图
        for (int i = 0; i < width; i++) {
            for (int j = 0; j < height; j++) {
                Color c = new Color(img.getRGB(i, j));
                int gray = (c.getRed() + c.getGreen() + c.getBlue()) / 3;
                histogram[gray]++;
            }
        }

        // 计算累积概率分布
        int[] cdf = new int[256];
        cdf[0] = histogram[0];
        for (int i = 1; i < 256; i++) {
            cdf[i] = cdf[i - 1] + histogram[i];
        }

        // 对每个像素进行直方图均衡化
        BufferedImage result = new BufferedImage(width, height, BufferedImage.TYPE_BYTE_GRAY);
        int num_pixels = width * height;
        for (int i = 0; i < width; i++) {
            for (int j = 0; j < height; j++) {
                Color c = new Color(img.getRGB(i, j));
                int gray = (c.getRed() + c.getGreen() + c.getBlue()) / 3;
                int new_gray = cdf[gray] * 255 / num_pixels;
                new_gray = Math.max(0, Math.min(255, new_gray)); // 确保灰度值在 0-255 范围内
                result.setRGB(i, j, new Color(new_gray, new_gray, new_gray).getRGB());
            }
        }
        return result;
    }

    // 中值滤波
    public static BufferedImage medianFiltering(BufferedImage img) {
        int width = img.getWidth();
        int height = img.getHeight();

        // 对每个像素进行中值滤波
        BufferedImage result = new BufferedImage(width, height, BufferedImage.TYPE_BYTE_GRAY);
        for (int i = 1; i < width - 1; i++) {
            for (int j = 1; j < height - 1; j++) {
                int[] gray_values = new int[9];
                int k = 0;
                for (int u = i - 1; u <= i + 1; u++) {
                    for (int v = j - 1; v <= j + 1; v++) {
                        Color c = new Color(img.getRGB(u, v));
                        int gray = (c.getRed() + c.getGreen() + c.getBlue()) / 3;
                        gray_values[k++] = gray;
                    }
                }
                // 对灰度值排序，取中值
                Arrays.sort(gray_values);
                int new_gray = gray_values[4];
                result.setRGB(i, j, new Color(new_gray, new_gray, new_gray).getRGB());
            }
        }
        return result;
    }

    // 将图像展示出来
    public static void displayImage(BufferedImage img, String title) {
        JFrame frame = new JFrame(title);
        JLabel label = new JLabel(new ImageIcon(img));
        frame.getContentPane().add(label, BorderLayout.CENTER);
        frame.pack();
        frame.setVisible(true);
    }

    // 将图像储存到本地
    public static void saveImage(BufferedImage img, String filename) throws IOException {
        ImageIO.write(img, "png", new File(filename));
    }
}

以上代码实现了三种常见的灰度图像增强操作：对比度增强、直方图均衡化和中值滤波。您可以根据需要选择其中一种或多种进行应用。同时，代码还实现了图像读取、结果展示、结果储存等功能，方便您进行使用和测试。