用chatgpt 生成的 测试了比较卡

   for (int y = 0; y < enlargedHeight; y++) {

                    for (int x = 0; x < enlargedWidth; x++) {

                        // 计算原始图像中对应的浮点坐标

                        float originalX = (float)x / (float)enlargedWidth * (float)originalWidth;

                        float originalY = (float)y / (float)enlargedHeight * (float)originalHeight;

                        // 进行双三次线性插值计算

                        QRgb interpolatedPixel = bicubicInterpolation(*img1, originalX, originalY);

                        // 将计算得到的像素值赋给新图像

                        newImg->setPixel(x, y, interpolatedPixel);

                    }

                }

// 原始图像大小

const int originalWidth = 32;

const int originalHeight = 24;

// 放大后的图像大小

const int enlargedWidth = 640;

const int enlargedHeight = 480;

// 双三次线性插值函数

QRgb bicubicInterpolation(const QImage& image, float x, float y) {

    // 计算四个最近的像素点的坐标

    int x1 = qFloor(x);

    int y1 = qFloor(y);

    int x2 = x1 + 1;

    int y2 = y1 + 1;

    // 计算插值权重

    float dx = x - x1;

    float dy = y - y1;

    // 获取四个最近的像素点的颜色值

    QRgb p11 = image.pixel(x1, y1);

    QRgb p12 = image.pixel(x1, y2);

    QRgb p21 = image.pixel(x2, y1);

    QRgb p22 = image.pixel(x2, y2);

    // 对四个像素点进行双三次线性插值计算

    float r = qRed(p11) * (1 - dx) * (1 - dy) + qRed(p21) * dx * (1 - dy) + qRed(p12) * (1 - dx) * dy + qRed(p22) * dx * dy;

    float g = qGreen(p11) * (1 - dx) * (1 - dy) + qGreen(p21) * dx * (1 - dy) + qGreen(p12) * (1 - dx) * dy + qGreen(p22) * dx * dy;

    float b = qBlue(p11) * (1 - dx) * (1 - dy) + qBlue(p21) * dx * (1 - dy) + qBlue(p12) * (1 - dx) * dy + qBlue(p22) * dx * dy;

    return qRgb(r, g, b);

}