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首先在Matlab中，我们可以用自带的图像处理函数，实现这种简单灰度的提取功能，也可以自己实现。下面贴出代码。

clear all;clc;

str = pwd;
index_dir = findstr( str, '\' );
str_temp = str( 1 : index_dir(end) );
filename = strcat( str_temp, '3_自制图片库\lena.bmp');

img = imread(filename);
imshow(img);

disp('------------调用matlab库函数--------------------');
%求最大灰度值
Max = max(max(img));
%求最小灰度值
Min = min(min(img));
%求平均灰度值
Avg = mean(mean(img));

fprintf('Max = %d\nMin = %d\nAvg = %f\n', Max, Min, Avg);

disp('--------------以下自己实现----------------------');
%%下面程序速度慢很多
[m, n] = size(img);
ma = 0;
mi = 255;
av = 0;
sum = 0;   % matlab中默认为double类型
for i = 1 : m
    for j = 1 : n
        if img(i, j) > ma
            ma = img(i, j);
        end
        if img(i, j) < mi
            mi = img(i, j);
        end
        sum = sum + double( img(i, j) );   % 注意这里：matlab中    double + uint8 = uint8  所以不做转换的话，数据会溢出 
    end
end
av = sum / (m * n);
fprintf('Max = %d\nMin = %d\nAvg = %f\n', ma, mi, av);

程序所用灰度图片：

下面是运行结果

------------调用matlab库函数--------------------
Max = 245
Min = 25
Avg = 124.042511
Time = 0.007000
--------------以下自己实现----------------------
Max = 245
Min = 25
Avg = 124.042511
Time = 3.539000

从时间运行上来看，自己实现的函数会慢很多，因为没有用到matlab语言的向量化编程，所以推荐使用第一种调用库函数的方法。

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下面简单用Opencv实现一下，也很简单：

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include <iostream>

using namespace std;
using namespace cv;

int main(int argc, char **argv)
{
	Mat img = imread("lena.bmp");

	if( img.empty() )
	{
		cout<< "找不到图像文件"<< endl;
		return -1;
	}

	if( img.channels() == 3 )
		cvtColor(img, img, CV_RGB2GRAY );

	int max = 0;
	int min = 255;
	double sum = 0;
	double avg = 0;

	double t = (double)getTickCount();   //计时开始
	for( int i = 0; i < img.rows; i++ )
	{
		for( int j = 0; j < img.cols; j++ )
		{
			//img.at<Vec3b>(i,j)[0]= 255;
			//img.at<Vec3b>(i,j)[1]= 255;
			//img.at<Vec3b>(i,j)[2]= 255;

			if ( img.at<uchar>(i, j) > max )
				max = img.at<uchar>(i, j);

			if ( img.at<uchar>(i, j) < min )
				min = img.at<uchar>(i, j);

			sum += img.at<uchar>(i, j);
		}
	}

	avg = sum / ( ( img.rows) * (img.cols) );
	t = ((double)getTickCount() - t)/getTickFrequency();  //计时结束

	cout<< "Max = "<< max<< endl;
	cout<< "Min = "<< min<< endl;
	cout<< "Avg = "<< avg<< endl;
	cout << "Times passed in seconds: " << t << endl;

	imshow("lena", img);

	waitKey();
	return 0;
}

运行结果:

Max = 245
Min = 25
Avg = 124.043
Times passed in seconds: 0.104919

从运行结果看：opencv必Matlab库函数也是慢很多。不过这里opencv中好像没有库函数来直接实现。有待发现。