clahe算法C++实现

由于CLAHE是一种图像增强算法，需要对图像进行像素级的处理，因此需要使用C语言来实现。

以下是CLAHE算法的C语言实现代码：

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

#define MAX_GRAY_LEVEL 256
#define CLIP_LIMIT  3.0

int height, width;
int clipLimit;
int nrTilesX, nrTilesY;
int tileSizeX, tileSizeY;
int *histogram;
int **lut;

int min(int a, int b) {
    return a < b ? a : b;
}

int max(int a, int b) {
    return a > b ? a : b;
}

int clip(int value, int minValue, int maxValue) {
    return max(min(value, maxValue), minValue);
}

void init() {
    tileSizeX = (int) ceil((double) width / nrTilesX);
    tileSizeY = (int) ceil((double) height / nrTilesY);
    clipLimit = (int) (CLIP_LIMIT * tileSizeX * tileSizeY / MAX_GRAY_LEVEL);

    histogram = (int*) malloc(MAX_GRAY_LEVEL * sizeof(int));

    lut = (int**) malloc(nrTilesX * sizeof(int*));
    for (int i = 0; i < nrTilesX; i++) {
        lut[i] = (int*) malloc(MAX_GRAY_LEVEL * sizeof(int));
    }
}

void calculateHistogram(unsigned char *image, int x, int y) {
    int startX = x * tileSizeX;
    int startY = y * tileSizeY;

    for (int i = 0; i < MAX_GRAY_LEVEL; i++) {
        histogram[i] = 0;
    }

    for (int i = 0; i < tileSizeY; i++) {
        for (int j = 0; j < tileSizeX; j++) {
            int pixelValue = image[(startY + i) * width + (startX + j)];
            histogram[pixelValue]++;
        }
    }
}

void clipHistogram() {
    int excess = 0;
    for (int i = 0; i < MAX_GRAY_LEVEL; i++) {
        if (histogram[i] > clipLimit) {
            excess += histogram[i] - clipLimit;
            histogram[i] = clipLimit;
        }
    }

    int average = excess / MAX_GRAY_LEVEL;
    int remainder = excess % MAX_GRAY_LEVEL;

    for (int i = 0; i < MAX_GRAY_LEVEL; i++) {
        histogram[i] += average;
    }

    for (int i = 0; i < remainder; i++) {
        histogram[i]++;
    }
}

void calculateLUT() {
    for (int i = 0; i < nrTilesX; i++) {
        calculateHistogram(image, i, 0);
        clipHistogram();

        int sum = 0;
        for (int j = 0; j < MAX_GRAY_LEVEL; j++) {
            sum += histogram[j];
            lut[i][j] = clip(sum * MAX_GRAY_LEVEL / tileSizeX / tileSizeY, 0, 255);
        }

        for (int j = 1; j < nrTilesY; j++) {
            calculateHistogram(image, i, j);
            clipHistogram();

            sum = 0;
            for (int k = 0; k < MAX_GRAY_LEVEL; k++) {
                sum += histogram[k];
                lut[i][k] = clip(sum * MAX_GRAY_LEVEL / tileSizeX / tileSizeY, 0, 255);
            }
        }
    }
}

void applyLUT(unsigned char *image) {
    for (int i = 0; i < height; i++) {
        for (int j = 0; j < width; j++) {
            int tileX = j / tileSizeX;
            int tileY = i / tileSizeY;
            int pixelValue = lut[tileX][image[i * width + j]];
            image[i * width + j] = (unsigned char) pixelValue;
        }
    }
}

void clahe(unsigned char *image, int _width, int _height, int _nrTilesX, int _nrTilesY) {
    image = (unsigned char*) malloc(width * height * sizeof(unsigned char));
    width = _width;
    height = _height;
    nrTilesX = _nrTilesX;
    nrTilesY = _nrTilesY;

    init();

    calculateLUT();

    applyLUT(image);
}

上述代码实现了CLAHE算法的主要流程，包括分割图像、计算直方图、限制直方图中像素数量、计算局部直方图均衡化后的灰度值映射表、应用灰度值映射表等步骤。

需要注意的是，由于CLAHE算法需要对图像进行像素级处理，因此在实现时需要使用指针和动态内存分配等操作。