C语言图像的反卷积代码

反卷积是一种复杂的图像处理技术，需要大量的数学知识和计算。以下是一个简单的C语言反卷积示例代码，仅供参考：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define M_PI 3.14159265358979323846

int main()
{
    int i, j, k, l, m, n;
    int width = 512, height = 512, channel = 3;
    double sigma = 1.0;
    double kernel[5][5] = {{0.023528, 0.033969, 0.038393, 0.033969, 0.023528},
                           {0.033969, 0.049045, 0.055432, 0.049045, 0.033969},
                           {0.038393, 0.055432, 0.062651, 0.055432, 0.038393},
                           {0.033969, 0.049045, 0.055432, 0.049045, 0.033969},
                           {0.023528, 0.033969, 0.038393, 0.033969, 0.023528}};
    double psf[512][512] = {0};
    double blur[512][512][3] = {0};
    double kernel_fft[512][512][3] = {0};
    double psf_fft[512][512][3] = {0};
    double result_fft[512][512][3] = {0};
    double result[512][512][3] = {0};

    // 生成高斯核
    for (i = 0; i < 5; i++) {
        for (j = 0; j < 5; j++) {
            kernel[i][j] *= exp(-((i - 2) * (i - 2) + (j - 2) * (j - 2)) / (2 * sigma * sigma));
        }
    }

    // 图像卷积
    for (k = 0; k < channel; k++) {
        for (i = 2; i < height - 2; i++) {
            for (j = 2; j < width - 2; j++) {
                double sum = 0;
                for (l = -2; l <= 2; l++) {
                    for (m = -2; m <= 2; m++) {
                        sum += kernel[l + 2][m + 2] * blur[i + l][j + m][k];
                    }
                }
                result[i][j][k] = sum;
            }
        }
    }

    // 生成模糊点扩散函数（PSF）
    for (i = 0; i < height; i++) {
        for (j = 0; j < width; j++) {
            double sum = 0;
            for (k = 0; k < channel; k++) {
                sum += (result[i][j][k] * result[i][j][k]);
            }
            psf[i][j] = sum;
        }
    }

    // 将PSF转换为频域
    for (k = 0; k < channel; k++) {
        for (i = 0; i < height; i++) {
            for (j = 0; j < width; j++) {
                psf_fft[i][j][k] = psf[i][j] * pow(-1, i + j);
            }
        }
        // 对行进行傅里叶变换
        for (i = 0; i < height; i++) {
            for (j = 0; j < width; j++) {
                double sum_real = 0, sum_imag = 0;
                for (l = 0; l < width; l++) {
                    sum_real += psf_fft[i][l][k] * cos(2 * M_PI * j * l / width);
                    sum_imag += psf_fft[i][l][k] * sin(2 * M_PI * j * l / width);
                }
                kernel_fft[i][j][k] = sum_real - sum_imag * I;
            }
        }
        // 对列进行傅里叶变换
        for (j = 0; j < width; j++) {
            for (i = 0; i < height; i++) {
                double sum_real = 0, sum_imag = 0;
                for (l = 0; l < height; l++) {
                    sum_real += kernel_fft[l][j][k] * cos(2 * M_PI * i * l / height);
                    sum_imag += kernel_fft[l][j][k] * sin(2 * M_PI * i * l / height);
                }
                psf_fft[i][j][k] = sum_real - sum_imag * I;
            }
        }
    }

    // 将模糊图像转换为频域
    for (k = 0; k < channel; k++) {
        for (i = 0; i < height; i++) {
            for (j = 0; j < width; j++) {
                result_fft[i][j][k] = result[i][j][k] * pow(-1, i + j);
            }
        }
        // 对行进行傅里叶变换
        for (i = 0; i < height; i++) {
            for (j = 0; j < width; j++) {
                double sum_real = 0, sum_imag = 0;
                for (l = 0; l < width; l++) {
                    sum_real += result_fft[i][l][k] * cos(2 * M_PI * j * l / width);
                    sum_imag += result_fft[i][l][k] * sin(2 * M_PI * j * l / width);
                }
                result_fft[i][j][k] = sum_real - sum_imag * I;
            }
        }
        // 对列进行傅里叶变换
        for (j = 0; j < width; j++) {
            for (i = 0; i < height; i++) {
                double sum_real = 0, sum_imag = 0;
                for (l = 0; l < height; l++) {
                    sum_real += result_fft[l][j][k] * cos(2 * M_PI * i * l / height);
                    sum_imag += result_fft[l][j][k] * sin(2 * M_PI * i * l / height);
                }
                result_fft[i][j][k] = sum_real - sum_imag * I;
            }
        }
    }

    // 对频域图像进行反卷积
    for (k = 0; k < channel; k++) {
        for (i = 0; i < height; i++) {
            for (j = 0; j < width; j++) {
                result_fft[i][j][k] /= psf_fft[i][j][k];
            }
        }
        // 将频域图像转换为空域图像
        // 对行进行傅里叶逆变换
        for (i = 0; i < height; i++) {
            for (j = 0; j < width; j++) {
                double sum_real = 0, sum_imag = 0;
                for (l = 0; l < width; l++) {
                    sum_real += result_fft[i][l][k] * cos(2 * M_PI * j * l / width);
                    sum_imag += result_fft[i][l][k] * sin(2 * M_PI * j * l / width);
                }
                result[i][j][k] = sum_real + sum_imag * I;
            }
        }
        // 对列进行傅里叶逆变换
        for (j = 0; j < width; j++) {
            for (i = 0; i < height; i++) {
                double sum_real = 0, sum_imag = 0;
                for (l = 0; l < height; l++) {
                    sum_real += result[l][j][k] * cos(2 * M_PI * i * l / height);
                    sum_imag += result[l][j][k] * sin(2 * M_PI * i * l / height);
                }
                result[i][j][k] = sum_real + sum_imag * I;
            }
        }
    }

    // 输出反卷积结果
    for (i = 0; i < height; i++) {
        for (j = 0; j < width; j++) {
            printf("%.2f %.2f %.2f\n", creal(result[i][j][0]), creal(result[i][j][1]), creal(result[i][j][2]));
        }
    }

    return 0;
}

该代码仅供参考，具体实现应根据实际需求进行修改和完善。