histogram_specification

最新推荐文章于 2025-05-13 12:08:40 发布

wangzaiqiqi

最新推荐文章于 2025-05-13 12:08:40 发布

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分类专栏：图像处理代码

本文链接：https://blog.youkuaiyun.com/thirteen_space/article/details/23708599

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本文介绍了一种图像处理中的直方图规范算法实现过程。该算法通过对输入图像进行直方图统计并应用逆变换，使得处理后的图像具有指定的直方图分布。涉及图像直方图计算、归一化累积直方图生成、查找表(LUT)转换等关键步骤。

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/***************************************************************************
 * Func: histogram_specification                                           *
 *                                                                         *
 * Desc: perform histogram specification on an input image                 *
 *                                                                         *
 * Params: buffer - pointer to input image                                 *
 *         number_of_pixel - total number of pixels in image               *
 *         desired_histogram - desired histogram of output image           *
 ***************************************************************************/

void histogram_specification(image_ptr buffer, unsigned long number_of_pixels,
			     unsigned long *desired_histogram)
    {
    float histogram[256];        /* image histogram */
    float sum_hist[256];         /* normalized sum of histogram elements */
    float scale_factor;          /* scaling factor used to normalize hist */
    float difference;            /* used to determine inverse transform */
    unsigned long i;             /* loop variable */
    unsigned long sum;           /* sum used to determine sum of hist */
    unsigned char inv_hist[256]; /* inverse histogram indices */
    int j;                       /* loop variable */
    int min;                     /* min difference computing inverse trans */

    /* clear histogram to 0 */
    for(i=0; i<256; i++)
	histogram[i]=0;

    /* calculate histogram */
    for(i=0; i<number_of_pixels; i++)
	histogram[buffer[i]]++;

    /* calculate normalized sum of hist */
    sum = 0.0;
    scale_factor = 255.0 / number_of_pixels;
    for(i=0; i<256; i++)
	{
	sum += histogram[i];
	sum_hist[i] = (sum * scale_factor) + 0.5;
	}

    /* transform image using new sum_hist as a LUT */
    for(i=0; i<number_of_pixels; i++)
	buffer[i] = (unsigned char) sum_hist[buffer[i]];

    /* calculate normalized sum of hist for desired histogram */
    sum = 0;
    scale_factor = 255.0 / number_of_pixels;
    for(i=0; i<256; i++)
	{
	sum += desired_histogram[i];
	sum_hist[i] = sum * scale_factor;
	}

    /* generate the inverse transform */
    for(i=0; i<256; i++)
	{
	difference = fabs(i - sum_hist[0]);
	min = 0;
	for(j=0; j<256; j++)
	   {
	   if(fabs(i-sum_hist[j]) < difference)
	       {
	       difference = fabs(i - sum_hist[j]);
	       min = j;
	       }
	   }
    inv_hist[i] = (unsigned char) min;
    }

    /* transform final image using inv_hist */
    for(i=0; i<number_of_pixels; i++)
	buffer[i] = inv_hist[buffer[i]];
    }

<256><256 >