图像处理相关算法之饱和度调整

调整饱和度的方法，代码如下：

void AdjustSatuation(cv::Mat &image, float adjust) {
    int width = image.cols, height = image.rows;
    for (int i = 0; i < height; ++i) {
        auto data = image.ptr<cv::Vec3b>(i);
        for (int j = 0; j < width; ++j) {
            float lum = data[j][0] * 0.299f + data[j][1] * 0.587f + data[j][2] * 0.114f;
            float maskB = std::max(0.0f, std::min(data[j][0] - lum, 255.0f)) / 255.0f;
            float maskG = std::max(0.0f, std::min(data[j][1] - lum, 255.0f)) / 255.0f;
            float maskR = std::max(0.0f, std::min(data[j][2] - lum, 255.0f)) / 255.0f;
            float lumMask = (1.0f - (maskB * 0.299f + maskG * 0.587f + maskR * 0.114f)) * adjust;
            data[j][0] = cv::saturate_cast<uchar>(data[j][0] * (1.0f + lumMask) - lum * lumMask);
            data[j][1] = cv::saturate_cast<uchar>(data[j][1] * (1.0f + lumMask) - lum * lumMask);
            data[j][2] = cv::saturate_cast<uchar>(data[j][2] * (1.0f + lumMask) - lum * lumMask);
        }
    }
}

调整前后对比：

原图

增加50%饱和度

减少50%饱和度

其中adjust取值范围为[-1,1]

另一种饱和度调整算法：

void AdjustSatuation(const cv::Mat &src, cv::Mat &dst, int percent) {
    dst.create(src.size(), src.type());
    int width = src.cols, height = src.rows;
    int channels = src.channels();
    if (channels != 3 || src.type() != CV_8UC3) {
        return;
    }
    float ratio1 = 1 / 255.0f;
    float adjust = std::max(std::min(percent, 100), -100) / 100.0f;
    for (int i = 0; i < height; ++i) {
        auto srcData = src.ptr<uchar>(i);
        auto dstData = dst.ptr<uchar>(i);
        for (int j = 0; j < width; ++j) {
            int j3 = j * 3;
            int r = srcData[j3 + 2];
            int g = srcData[j3 + 1];
            int b = srcData[j3];
            int maxV = std::max(r, std::max(g, b));
            int minV = std::min(r, std::min(g, b));
            float delta = (maxV - minV) * ratio1;
            if (delta < 1e-3f) {
                dstData[j3] = b;
                dstData[j3 + 1] = g;
                dstData[j3 + 2] = r;
                continue;
            }
            float value = (maxV + minV) * ratio1;
            float L = value / 2.0f;
            float S = delta / value;
            if (L >= 0.5f) {
                S = delta / (2.0f - value);
            }
            if (percent >= 0) {
                float alpha = 1.0f - adjust;
                if (adjust + S >= 1) {
                    alpha = S;
                }
                alpha = 1.0f / alpha - 1.0f;
                dstData[j3] = cv::saturate_cast<uchar>(b + (b - L * 255.0f) * alpha);
                dstData[j3 + 1] = cv::saturate_cast<uchar>(g + (g - L * 255.0f) * alpha);
                dstData[j3 + 2] = cv::saturate_cast<uchar>(r + (r - L * 255.0f) * alpha);
            }
            else {
                float alpha = 1.0f + adjust;
                dstData[j3] = cv::saturate_cast<uchar>(L * 255.0f + (b - L * 255.0f) * alpha);
                dstData[j3 + 1] = cv::saturate_cast<uchar>(L * 255.0f + (g - L * 255.0f) * alpha);
                dstData[j3 + 2] = cv::saturate_cast<uchar>(L * 255.0f + (r - L * 255.0f) * alpha);
            }
        }
    }
}

调整后的图像结果为：

增加50%的饱和度

减少50%的饱和度