編程環(huán)境：

VS + OpenCV + C++
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內(nèi)容：

對(duì)比度及亮度的基本調(diào)整原理公式為：g(i,j) = af(i,j) + b*
->g 變換后圖像
->f 變換前原圖
->a 用于控制對(duì)比度
->b 用于控制亮度

1.1：對(duì)比度調(diào)整

? ? ? ?設(shè)計(jì)一個(gè)Sigmoid函數(shù)，實(shí)現(xiàn)對(duì)圖像的對(duì)比度調(diào)整；使用opencv窗口系統(tǒng)的slider控件，交互改變Sigmoid函數(shù)的參數(shù)，實(shí)現(xiàn)不同程度的對(duì)比度調(diào)整；

1.2：背景相減

? ? ? ?對(duì)圖像I和對(duì)應(yīng)的背景圖B，基于背景相減檢測(cè)I中的前景區(qū)域，設(shè)法改進(jìn)結(jié)果。

一、線性函數(shù)對(duì)比度亮度調(diào)節(jié)：

code

#include<opencv2/core/core.hpp>
#include<opencv2/imgcodecs.hpp>
#include<opencv2/core/saturate.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<iostream> 

using namespace std;
using namespace cv;

int main(int, char** argv)
{
    double alpha = 1.0; /*< Simple contrast control */
    int beta = 0;       /*< Simple brightness control */

    Mat image = imread("a.png");
    //cvtColor(image, image, CV_BGR2GRAY);//轉(zhuǎn)為灰度圖
    Mat new_image = Mat::zeros(image.size(), image.type());

    cout << " Basic Linear Transforms " << endl;
    cout << "-------------------------" << endl;
    cout << "* Enter the alpha value [1.0-3.0]: "; cin >> alpha;
    cout << "* Enter the beta value [0-100]: ";    cin >> beta;

    for (int y = 0; y < image.rows; y++) {
        for (int x = 0; x < image.cols; x++) {
            for (int c = 0; c < 3; c++) {
                new_image.at<Vec3b>(y, x)[c] = saturate_cast<uchar>(alpha*(image.at<Vec3b>(y, x)[c]) + beta);
            }
        }
    }

    namedWindow("Original Image", WINDOW_AUTOSIZE);
    namedWindow("New Image", WINDOW_AUTOSIZE);

    imshow("Original Image", image);
    imshow("New Image", new_image);

    waitKey();
    return 0;
}

二、sigmoid函數(shù)對(duì)比度亮度調(diào)節(jié)：

image.png

（1）首先應(yīng)用公式

image.png

（引用自論文A New Approach for Contrast Enhancement Using Sigmoid Function——Naglaa Hassan and Norio Akamatsul）對(duì)每個(gè)像素點(diǎn)進(jìn)行處理，發(fā)現(xiàn)效果并不好，容易高亮丟失紋理（受參數(shù)C的影響太大），實(shí)用效果差如下圖：

image.png

image.png

（2）自行設(shè)計(jì)調(diào)整函數(shù)：

t = I(x,y) / 255.00 * c_p * 0.1; //c_p為界面可動(dòng)態(tài)調(diào)整參數(shù)
O(x,y) = I(x,y) ( ( 1.00/ ( 1.00+exp ( -t) ) + 0.5 ) + b_value - 100)
應(yīng)用新公式測(cè)試效果如下：

image.png

（3）發(fā)現(xiàn)還是會(huì)有一些紋理丟失，思考后認(rèn)為255/200-1<0.3，所以應(yīng)該將0.5這個(gè)參數(shù)調(diào)低至0.3，調(diào)低為0.3后進(jìn)行測(cè)試效果較好，達(dá)到預(yù)期效果。：

image.png

image.png

code:

#include <opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include"opencv2/imgproc/imgproc.hpp"
#include<math.h>
#include <iostream>

using namespace std;
using namespace cv;

static void ContrastAndBright(int, void *);

int c_p;
int b_value;
Mat s_image, t_imag;

int main()
{
    s_image = imread("22.png");

    if (!s_image.data)
    {
        cout << "圖片讀取失??！" << endl;
        return false;
    }
    t_imag = Mat::zeros(s_image.size(), s_image.type());

    c_p = 20;
    b_value = 100;

    namedWindow("效果圖窗口", WINDOW_AUTOSIZE);

    createTrackbar("對(duì)比度", "效果圖窗口", &c_p, 200, ContrastAndBright);
    createTrackbar("亮  度", "效果圖窗口", &b_value, 200, ContrastAndBright);

    ContrastAndBright(c_p, 0);
    ContrastAndBright(b_value, 0);

    waitKey(0);
    return 0;

}

static void ContrastAndBright(int, void *)
{

    namedWindow("窗口02", WINDOW_AUTOSIZE);
    // g_dstImage(i,j) =a*g_srcImage(i,j) + b

    for (int y = 0; y < s_image.rows; y++)
    {
        for (int x = 0; x < s_image.cols; x++)
        {
            for (int c = 0; c < 3; c++)
            {
                //0<=c_p<=200
                double t = ((s_image.at<Vec3b>(y, x)[c] - 127) / 225.00)*c_p*0.1;
                t_imag.at<Vec3b>(y, x)[c] = saturate_cast<uchar>(s_image.at<Vec3b>(y, x)[c] * ((1.00 / (1.00 + exp(-t))) + 0.3) + b_value - 100);
                //int t = s_image.at<Vec3b>(y, x)[c];
                //t_imag.at<Vec3b>(y, x)[c] = saturate_cast<uchar>(t * ((c_p*0.1 / (1.00 + exp(-t))) + 1));
            }
        }
    }
    imshow("窗口02", s_image);
    imshow("效果圖窗口", t_imag);
}

三、背景相減實(shí)驗(yàn)：

1、相減得到圖一，下半未很好顯示，而后定義閾值后與（f_image(x,y)[c] - b_image(x,y)[c]）比較，對(duì)RGB三通道分別賦值為0或255得到下圖效果：

圖一

圖二（閾值=5）

圖三（閾值=30）

圖四（閾值=50）

2、調(diào)整改進(jìn)算法，原理如下：

對(duì)于去除噪聲點(diǎn)，可以考慮使用中指濾波，對(duì)椒鹽噪聲有較好效果

image.png

50

75

100

150

code

#include <opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include"opencv2/imgproc/imgproc.hpp"
#include<math.h>
#include <iostream>

using namespace std;
using namespace cv;

int maind22(){
    Mat f_image, b_image;

    f_image = imread("123.png");
    b_image = imread("123b.png");

    //Mat gray_f;
    //cvtColor(f_image, gray_f, CV_BGR2GRAY);  //彩色圖片轉(zhuǎn)換成黑白圖片



    if (!f_image.data||!b_image.data)
    {
        cout << "讀取圖片失??！" << endl;
        return -1;
    }

    if (f_image.rows != b_image.rows || f_image.cols != b_image.cols)
        cout << "前景圖與后景圖尺寸不符，無(wú)法相減！" << endl;

    Mat t_image = Mat::zeros(f_image.size(), f_image.type());

    namedWindow("<效果圖窗口>", WINDOW_AUTOSIZE);

    double sum = 0.0;

    for (int y = 0; y < f_image.rows; y++)
    {
        for (int x = 0; x < f_image.cols; x++)
        {
            sum = 0.0;
            for (int c = 0; c < 3; c++)
            {
                sum += pow((f_image.at<Vec3b>(y, x)[c] - b_image.at<Vec3b>(y, x)[c]), 2);   
            }
            sum = sqrt(sum);
            if (sum >= 75) {
                for (int c = 0; c < 3; c++)
                {
                    t_image.at<Vec3b>(y, x)[c] = saturate_cast < uchar>(255);
                }
            }
            else {
                for (int c = 0; c < 3; c++)
                {
                    t_image.at<Vec3b>(y, x)[c] = saturate_cast < uchar>(0);
                }
            }               
        }
    }

    //cvtColor(t_image, t_image, CV_BGR2GRAY);
    imshow("<效果圖窗口>", t_image);
    waitKey(0);
    return 0;
}