彩色图像--图像增强图像平滑，

文章由LinuxBoy分享于2019-03-27 08:03:03热评（86）

彩色图像--图像增强图像平滑，

学习DIP第71天
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开篇废话

最近博客数量变多，访问量也一路上升，虽然目的并不是要多少访问量，但看着知识一点点的积累，而且有人认可，心情相当不错的。

算法原理

对灰度图像进行平滑的文章：
1、均值，高斯平滑
2、双边滤波
3、中值滤波

这些算法在彩色图像中应用有两种，一种是对RGB全部分量进行分别处理，然后将个处理过的分量进行合并，得到彩色图像。另一种是对HSI的I分量进行处理，同样能得到平滑效果，对彩色图像的平滑这里只是最简单的介绍，如果想写个美图秀秀，需要在找几篇论文来学下。

代码

/*********************************************************************************************************************/

void SmoothRGB(RGB *src,RGB *dst,int width,int height,int m_width,int m_height,double param1,double param2,int Smooth_type){
    double *chanel_r=(double*)malloc(sizeof(double)*width*height);
    double *chanel_g=(double*)malloc(sizeof(double)*width*height);
    double *chanel_b=(double*)malloc(sizeof(double)*width*height);
    double *chanel_r_dst=(double*)malloc(sizeof(double)*width*height);
    double *chanel_g_dst=(double*)malloc(sizeof(double)*width*height);
    double *chanel_b_dst=(double*)malloc(sizeof(double)*width*height);
    Split(src, chanel_r, chanel_g, chanel_b, width, height);
    switch (Smooth_type) {
        case SMOOTH_GAUSSIAN:{
            GaussianFilter(chanel_r, chanel_r_dst, width, height, m_width, m_height, param1);
            GaussianFilter(chanel_g, chanel_g_dst, width, height, m_width, m_height, param1);
            GaussianFilter(chanel_b, chanel_b_dst, width, height, m_width, m_height, param1);
            break;
        }
        case SMOOTH_MEDIAN:{
            MedianFilter(chanel_r, chanel_r_dst, width, height, m_width, m_height);
            MedianFilter(chanel_g, chanel_g_dst, width, height, m_width, m_height);
            MedianFilter(chanel_b, chanel_b_dst, width, height, m_width, m_height);
            break;
        }
        case SMOOTH_BILATERAL:{
            BilateralFilter(chanel_r, chanel_r_dst, width, height, m_width, m_height, param1, param2);
            BilateralFilter(chanel_g, chanel_g_dst, width, height, m_width, m_height, param1, param2);
            BilateralFilter(chanel_b, chanel_b_dst, width, height, m_width, m_height, param1, param2);
            break;
        }
        case SMOOTH_MEAN:{
            MeanFilter(chanel_r, chanel_r_dst, width, height, m_width, m_height);
            MeanFilter(chanel_g, chanel_g_dst, width, height, m_width, m_height);
            MeanFilter(chanel_b, chanel_b_dst, width, height, m_width, m_height);
            break;

        }
        default:
            break;
    }

    Merge(chanel_r_dst, chanel_g_dst, chanel_b_dst, dst, width, height);
    free(chanel_r);
    free(chanel_g);
    free(chanel_b);
    free(chanel_r_dst);
    free(chanel_g_dst);
    free(chanel_b_dst);
}

/*********************************************************************************************************************/

void SmoothHSI(HSI *src,HSI *dst,int width,int height,int m_width,int m_height,double param1,double param2,int Smooth_type){
    double *chanel_i=(double*)malloc(sizeof(double)*width*height);
    double *chanel_i_dst=(double*)malloc(sizeof(double)*width*height);
    Split(src, NULL, NULL, chanel_i, width, height);
    switch (Smooth_type) {
        case SMOOTH_GAUSSIAN:{
            GaussianFilter(chanel_i, chanel_i_dst, width, height, m_width, m_height, param1);
            break;
        }
        case SMOOTH_MEDIAN:{
            MedianFilter(chanel_i, chanel_i_dst, width, height, m_width, m_height);
            break;
        }
        case SMOOTH_BILATERAL:{
            BilateralFilter(chanel_i, chanel_i_dst, width, height, m_width, m_height, param1, param2);
            break;
        }
        case SMOOTH_MEAN:{
            MeanFilter(chanel_i, chanel_i_dst, width, height, m_width, m_height);
            break;
        }
        default:
            break;
    }
    for(int i=0;i<width*height;i++){
        dst[i].c1=src[i].c1;
        dst[i].c2=src[i].c2;
        dst[i].c3=chanel_i_dst[i];
    }
    free(chanel_i);
    free(chanel_i_dst);
}

/*********************************************************************************************************************/