探究降噪合理性

区分度

合理的降噪结果应该是本身区分度大的地方更大，让本身区分度小的地方更小

因此我们拿一张原图，跟降噪后的的图片进行试验（这里就不给出图片样例了）

但是可以看一下结果：

img

代码如下：

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# python3.8
# utf-8
import cv2 as cv
import matplotlib.pyplot as plt


# 考虑位置
def division(_8area):
    result = [0] * 8
    p = [_8area[0][0], _8area[0][1], _8area[0][2], _8area[1][2],
         _8area[2][2], _8area[2][1], _8area[2][0], _8area[1][0]]
    d = [int(p[0]) - int(p[1]), int(p[1]) - int(p[2]), int(p[2]) - int(p[3]), int(p[3]) - int(p[4]),
         int(p[4]) - int(p[5]), int(p[5]) - int(p[6]), int(p[6]) - int(p[7]), int(p[7]) - int(p[0])]
    max_index = 0
    min_index = 0
    for i in range(1, 8):
        if d[i] > d[max_index]:
            max_index = i
        if d[i] < d[min_index]:
            min_index = i
    if max_index == min_index:
        pass
    elif max_index > min_index:
        for i in range(0, min_index + 1):
            result[i] = 1
        for i in range(min_index + 1, max_index + 1):
            result[i] = 2
        if max_index < 7:
            for i in range(max_index + 1, 8):
                result[i] = 1
    elif max_index < min_index:
        for i in range(0, max_index + 1):
            result[i] = 2
        for i in range(max_index + 1, min_index + 1):
            result[i] = 1
        if min_index < 7:
            for i in range(min_index + 1, 8):
                result[i] = 2
    # 求区分度
    max1 = 0
    for i in range(8):
        if result[i] == 1 and p[i] > max1:
            max1 = p[i]
    min2 = 0
    for i in range(8):
        if result[i] == 2 and p[i] < min2:
            min2 = p[i]
    return max1 - min2


# 以2X2为单位求区分度
def get_difference(img):
    row, col = img.shape
    result = [0] * 256
    for i in range(256):
        result[i] = 0
    for i in range(1, row - 1):
        for j in range(1, col - 1):
            # _8_area = type(img_channel)
            _8_area = [[0] * 3 for i in range(3)]
            _8_area[0][0] = img[i - 1][j - 1]
            _8_area[0][1] = img[i - 1][j]
            _8_area[0][2] = img[i - 1][j + 1]
            _8_area[1][2] = img[i][j + 1]
            _8_area[2][2] = img[i + 1][j + 1]
            _8_area[2][1] = img[i + 1][j]
            _8_area[2][0] = img[i + 1][j - 1]
            _8_area[1][0] = img[i][j - 1]
            result[division(_8_area)] += 1
    return result


def main():
    # 图像地址
    origin_address = "origin.png"
    denoise_address = "denoise.png"

    # 以灰度方式读入图像
    origin_img = cv.imread(origin_address, 0)
    denoise_img = cv.imread(denoise_address, 0)

    # 求区分度
    origin_difference = get_difference(origin_img)
    denoise_difference = get_difference(denoise_img)

    # 制图
    plt.plot(origin_difference, color='red')
    plt.plot(denoise_difference, color='blue')
    # 用来正常显示中文标签
    plt.rcParams['font.sans-serif'] = ['SimHei']
    # 用来正常显示负号
    plt.rcParams['axes.unicode_minus'] = False
    # 标题
    plt.title('区分度折线图(红色原图/蓝色处理图)', fontsize=24, color='black')
    plt.show()


if __name__ == '__main__':
    main()

差异度

差异度为3X3小区块内，最大值减去最小值，将得到的值即为差异度，将各差异度累加（大小范围在0~255），制成折线图

效果图：

img

代码如下：

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python# python3.8
# utf-8
import cv2 as cv
import matplotlib.pyplot as plt


def division(_8area):
    result = [0] * 8
    p = [_8area[0][0], _8area[0][1], _8area[0][2], _8area[1][2],
         _8area[2][2], _8area[2][1], _8area[2][0], _8area[1][0]]

    # 求差异度
    max1 = 0
    for i in range(8):
        if p[i] > max1:
            max1 = p[i]
    min2 = 0
    for i in range(8):
        if p[i] < min2:
            min2 = p[i]
    return max1 - min2


# 求差异度
def get_d(img):
    row, col = img.shape
    result = [0] * 256
    for i in range(256):
        result[i] = 0
    for i in range(1, row - 1):
        for j in range(1, col - 1):
            # _8_area = type(img_channel)
            _8_area = [[0] * 3 for i in range(3)]
            _8_area[0][0] = img[i - 1][j - 1]
            _8_area[0][1] = img[i - 1][j]
            _8_area[0][2] = img[i - 1][j + 1]
            _8_area[1][2] = img[i][j + 1]
            _8_area[2][2] = img[i + 1][j + 1]
            _8_area[2][1] = img[i + 1][j]
            _8_area[2][0] = img[i + 1][j - 1]
            _8_area[1][0] = img[i][j - 1]
            result[division(_8_area)] += 1
    return result


def main():
    # 图像地址
    origin_address = "origin.png"
    denoise_address = "denoise.png"

    # 以灰度方式读入图像
    origin_img = cv.imread(origin_address, 0)
    denoise_img = cv.imread(denoise_address, 0)

    # 求差异度
    d_origin = get_d(origin_img)
    d_denoise = get_d(denoise_img)

    # 制图
    x = [0] * 256
    for i in range(256):
        x[i] = i
    plt.plot(x, d_origin, color='red')
    plt.plot(x, d_denoise, color='blue')
    # plt.axis([1, 60, -1, 50])
    # 用来正常显示中文标签
    plt.rcParams['font.sans-serif'] = ['SimHei']
    # 用来正常显示负号
    plt.rcParams['axes.unicode_minus'] = False
    # 标题
    plt.title('差异度折线图(红色原图/蓝色处理图)', fontsize=24, color='black')
    plt.show()


if __name__ == '__main__':
    main()