逆滤波c++不用库 代码

以下是一个简单的逆滤波 C++ 代码示例，不依赖于任何库：

#include <iostream>

// 定义图像宽度和高度
const int WIDTH = 512;
const int HEIGHT = 512;

// 计算二维傅里叶变换
void dft2D(double** input, double** output, int width, int height, int dir)
{
    double re, im;
    double pi2 = dir * 2.0 * 3.14159265358979323846;
    for(int i = 0; i < height; i++) {
        for(int j = 0; j < width; j++) {
            re = im = 0.0;
            for(int k = 0; k < height; k++) {
                for(int l = 0; l < width; l++) {
                    double tmp = pi2 * (double)(j * k + i * l) / (double)(width * height);
                    re += input[k][l] * cos(tmp);
                    im -= input[k][l] * sin(tmp);
                }
            }
            output[i][j] = (dir == 1) ? (re / (double)(width * height)) : (re);
        }
    }
}

// 计算高斯滤波器
void gaussianFilter(double** filter, int width, int height, double sigma)
{
    double sum = 0.0;
    for(int i = 0; i < height; i++) {
        for(int j = 0; j < width; j++) {
            filter[i][j] = exp(-(double)(i * i + j * j) / (2.0 * sigma * sigma));
            sum += filter[i][j];
        }
    }
    for(int i = 0; i < height; i++) {
        for(int j = 0; j < width; j++) {
            filter[i][j] /= sum;
        }
    }
}

// 对图像进行逆滤波
void inverseFilter(double** input, double** output, int width, int height, double** filter, int filter_width, int filter_height)
{
    double** input_freq = new double*[height];
    double** filter_freq = new double*[height];
    double** output_freq = new double*[height];

    for(int i = 0; i < height; i++) {
        input_freq[i] = new double[width];
        filter_freq[i] = new double[width];
        output_freq[i] = new double[width];
    }

    // 对输入图像和滤波器进行傅里叶变换
    dft2D(input, input_freq, width, height, 1);
    dft2D(filter, filter_freq, filter_width, filter_height, 1);

    // 逆滤波
    for(int i = 0; i < height; i++) {
        for(int j = 0; j < width; j++) {
            double re = input_freq[i][j] * filter_freq[i][j];
            double im = 0.0;
            output_freq[i][j] = re / (re * re + im * im + 0.1);
        }
    }

    // 对输出图像进行傅里叶变换
    dft2D(output_freq, output, width, height, -1);

    for(int i = 0; i < height; i++) {
        delete[] input_freq[i];
        delete[] filter_freq[i];
        delete[] output_freq[i];
    }
    delete[] input_freq;
    delete[] filter_freq;
    delete[] output_freq;
}

int main()
{
    // 生成一个测试图像
    double** input_image = new double*[HEIGHT];
    for(int i = 0; i < HEIGHT; i++) {
        input_image[i] = new double[WIDTH];
        for(int j = 0; j < WIDTH; j++) {
            input_image[i][j] = (i + j) % 256;
        }
    }

    // 生成一个高斯滤波器
    int filter_width = 5;
    int filter_height = 5;
    double sigma = 1.0;
    double** filter = new double*[filter_height];
    for(int i = 0; i < filter_height; i++) {
        filter[i] = new double[filter_width];
    }
    gaussianFilter(filter, filter_width, filter_height, sigma);

    // 对图像进行逆滤波
    double** output_image = new double*[HEIGHT];
    for(int i = 0; i < HEIGHT; i++) {
        output_image[i] = new double[WIDTH];
    }
    inverseFilter(input_image, output_image, WIDTH, HEIGHT, filter, filter_width, filter_height);

    // 输出结果
    for(int i = 0; i < HEIGHT; i++) {
        for(int j = 0; j < WIDTH; j++) {
            std::cout << output_image[i][j] << " ";
        }
        std::cout << std::endl;
    }

    // 释放内存
    for(int i = 0; i < HEIGHT; i++) {
        delete[] input_image[i];
        delete[] output_image[i];
    }
    delete[] input_image;
    delete[] output_image;

    for(int i = 0; i < filter_height; i++) {
        delete[] filter[i];
    }
    delete[] filter;

    return 0;
}

该代码生成一个测试图像，然后生成一个高斯滤波器，并对测试图像进行逆滤波，最后输出结果。请注意，此代码中的傅里叶变换实现非常简单，并且可能不适用于大型图像。