c ++实现RSS图像增强算法实现

RSS（Retinex-based Image Enhancement Algorithm with Soft-color Restoration）是一种基于Retinex理论的图像增强算法。下面是C语言实现RSS图像增强算法的步骤：

1. 读入一幅图像，将其转换为灰度图像。

2. 对图像进行高斯模糊，去除图像中的噪声。

3. 计算图像的全局平均值、全局方差以及局部平均值和局部方差。

4. 根据Retinex理论，计算图像的反射率和亮度。

5. 对反射率进行伽马校正，增强图像的对比度。

6. 进行颜色恢复，将图像恢复到原来的颜色。

下面是C语言实现RSS图像增强算法的代码示例：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define PI 3.1415926535

/* 高斯函数 */
double Gaussian(double x, double sigma) {
    return exp(-(x * x) / (2.0 * sigma * sigma)) / (sqrt(2.0 * PI) * sigma);
}

/* 高斯模糊 */
void GaussianBlur(double* img, int width, int height, double sigma) {
    double* tmp = (double*)malloc(sizeof(double) * width * height);
    int r = ceil(3.0 * sigma);
    for (int y = 0; y < height; y++) {
        for (int x = 0; x < width; x++) {
            double sum = 0.0, wsum = 0.0;
            for (int j = -r; j <= r; j++) {
                int yy = y + j;
                if (yy < 0 || yy >= height) continue;
                for (int i = -r; i <= r; i++) {
                    int xx = x + i;
                    if (xx < 0 || xx >= width) continue;
                    double w = Gaussian(sqrt((double)(i * i + j * j)), sigma);
                    sum += w * img[yy * width + xx];
                    wsum += w;
                }
            }
            tmp[y * width + x] = sum / wsum;
        }
    }
    for (int i = 0; i < width * height; i++) img[i] = tmp[i];
    free(tmp);
}

/* 计算全局平均值和全局方差 */
void GlobalMeanAndVar(double* img, int width, int height, double* mean, double* var) {
    double sum = 0.0, sum2 = 0.0;
    for (int i = 0; i < width * height; i++) {
        sum += img[i];
        sum2 += img[i] * img[i];
    }
    *mean = sum / (double)(width * height);
    *var = sum2 / (double)(width * height) - (*mean) * (*mean);
}

/* 计算局部平均值和局部方差 */
void LocalMeanAndVar(double* img, int width, int height, int size, double* mean, double* var) {
    int r = size / 2;
    double sum = 0.0, sum2 = 0.0;
    for (int y = 0; y < height; y++) {
        for (int x = 0; x < width; x++) {
            double tmp = 0.0, tmp2 = 0.0;
            int cnt = 0;
            for (int j = -r; j <= r; j++) {
                int yy = y + j;
                if (yy < 0 || yy >= height) continue;
                for (int i = -r; i <= r; i++) {
                    int xx = x + i;
                    if (xx < 0 || xx >= width) continue;
                    tmp += img[yy * width + xx];
                    tmp2 += img[yy * width + xx] * img[yy * width + xx];
                    cnt++;
                }
            }
            double mn = tmp / (double)cnt, vr = tmp2 / (double)cnt - mn * mn;
            sum += mn, sum2 += vr;
        }
    }
    *mean = sum / (double)(width * height);
    *var = sum2 / (double)(width * height);
}

/* 计算反射率 */
void Reflectance(double* img, int width, int height, double sigma) {
    double mean1, var1, mean2, var2;
    GlobalMeanAndVar(img, width, height, &mean1, &var1);
    LocalMeanAndVar(img, width, height, width / 8, &mean2, &var2);
    for (int i = 0; i < width * height; i++) {
        double r = img[i] * (var1 / var2);
        img[i] = pow(r, 0.7) * pow(mean2 / mean1, 0.3);
    }
}

/* 伽马校正 */
void GammaCorrection(double* img, int width, int height, double gamma) {
    for (int i = 0; i < width * height; i++) {
        img[i] = pow(img[i], gamma);
    }
}

/* 颜色恢复 */
void ColorRestoration(double* img, int width, int height, double sigma) {
    double* r = (double*)malloc(sizeof(double) * width * height);
    double* g = (double*)malloc(sizeof(double) * width * height);
    double* b = (double*)malloc(sizeof(double) * width * height);
    for (int i = 0; i < width * height; i++) {
        r[i] = img[i];
        g[i] = img[i];
        b[i] = img[i];
    }
    GaussianBlur(r, width, height, sigma);
    GaussianBlur(g, width, height, sigma);
    GaussianBlur(b, width, height, sigma);
    for (int i = 0; i < width * height; i++) {
        double maxval = r[i];
        if (g[i] > maxval) maxval = g[i];
        if (b[i] > maxval) maxval = b[i];
        if (maxval > 1.0) {
            r[i] /= maxval;
            g[i] /= maxval;
            b[i] /= maxval;
        }
        img[i] = img[i] / (r[i] + g[i] + b[i]);
    }
    free(r), free(g), free(b);
}

/* 主函数 */
int main() {
    int width = 512, height = 512;
    double* img = (double*)malloc(sizeof(double) * width * height);
    for (int y = 0; y < height; y++) {
        for (int x = 0; x < width; x++) {
            double xx = (double)x / (double)width, yy = (double)y / (double)height;
            img[y * width + x] = exp(-(xx - 0.5) * (xx - 0.5) * 100.0 - (yy - 0.5) * (yy - 0.5) * 100.0);
        }
    }
    GaussianBlur(img, width, height, 10.0);
    Reflectance(img, width, height, 10.0);
    GammaCorrection(img, width, height, 0.7);
    ColorRestoration(img, width, height, 10.0);
    FILE* fp = fopen("output.raw", "wb");
    fwrite(img, sizeof(double), width * height, fp);
    fclose(fp);
    free(img);
    return 0;
}

以上代码将生成一个512x512的图像，并对其进行RSS图像增强算法处理，最后将结果保存为二进制文件output.raw。可以使用图像处理软件（如ImageJ）打开这个文件进行查看。