Java データ構造とアルゴリズム: 画像処理の実用的な最適化

import java.awt.image.BufferedImage;

public class ImageSharpener {

    public static BufferedImage sharpen(BufferedImage image) {

        // 获取图像尺寸
        int width = image.getWidth();
        int height = image.getHeight();

        // 保存原始图像像素
        int[][] originalPixels = new int[width][height];
        for (int i = 0; i < width; i++) {
            for (int j = 0; j < height; j++) {
                originalPixels[i][j] = image.getRGB(i, j);
            }
        }

        // 创建卷积核
        int[][] kernel = {
            {-1, -1, -1},
            {-1, 9, -1},
            {-1, -1, -1}
        };

        // 遍历每个像素
        for (int i = 1; i < width - 1; i++) {
            for (int j = 1; j < height - 1; j++) {

                // 应用卷积核
                int newPixel = 0;
                for (int m = -1; m <= 1; m++) {
                    for (int n = -1; n <= 1; n++) {
                        newPixel += originalPixels[i + m][j + n] * kernel[m + 1][n + 1];
                    }
                }

                // 剪切新像素值以限制范围为 0-255
                newPixel = Math.max(0, Math.min(255, newPixel));

                // 设置新像素值
                image.setRGB(i, j, newPixel);
            }
        }

        return image;
    }
}

import java.util.HashMap;

public class ImageDatabase {

    private HashMap<String, BufferedImage> images;

    public ImageDatabase() {
        images = new HashMap<String, BufferedImage>();
    }

    public void addImage(String name, BufferedImage image) {
        images.put(name, image);
    }

    public BufferedImage getImage(String name) {
        return images.get(name);
    }
}

import java.util.concurrent.ArrayBlockingQueue;

public class ImageProcessingQueue {

    private ArrayBlockingQueue<BufferedImage> images;

    public ImageProcessingQueue() {
        images = new ArrayBlockingQueue<BufferedImage>(100);
    }

    public void addImage(BufferedImage image) {
        images.offer(image);
    }

    public BufferedImage getNextImage() {
        return images.poll();
    }
}