【图像处理】python实现图像锐化边缘检测算子（Robert、Sobel、Prewitt、Laplacian算子）_编写实现图像锐化的函数,可设置robert、sebel、laplace算子,对图像进行锐化处理

作者：AllinToyou | 2024-02-09 17:49:02

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编写实现图像锐化的函数,可设置robert、sebel、laplace算子,对图像进行锐化处理

一、Robert算子

罗伯特梯度法(Robert Gradient), 是一种交叉差分方法。其数学表达式可近似为：

G[f(x, y)] ≈|f(i, j)-f(i+1, j+1) |+|f(i+1, j)-f(i, j+1)|


################################################
#           Robert算子
################################################
def robert_filter(image):
    h = image.shape[0]
    w = image.shape[1]
    image_new = np.zeros(image.shape, np.uint8)
    for i in range(1, h-1):
        for j in range(1, w-1):
            image_new[i][j] = np.abs((image[i][j]-image[i+1][j+1])) + np.abs(image[i+1][j]-image[i][j+1])
    return image_new

二、Sobel算子

采用梯度微分锐化图像，同时会使噪声、条纹等得到增强， Sobel算子则在一定程度上克服了这个问题。Sobel算子法的基本原理是：计算3×3窗口的灰度，将其作为变换后图像g(i, j)的灰度。公式如下：


################################################
#           Sobel算子
################################################
def sobel_filter(image):
    h = image.shape[0]
    w = image.shape[1]
    image_new = np.zeros(image.shape, np.uint8)
 
    for i in range(1, h-1):
        for j in range(1, w-1):
            sx = (image[i + 1][j - 1] + 2 * image[i + 1][j] + image[i + 1][j + 1]) - \
                 (image[i - 1][j - 1] + 2 * image[i - 1][j] + image[i - 1][j + 1])
            sy = (image[i - 1][j + 1] + 2 * image[i][j + 1] + image[i + 1][j + 1]) - \
                 (image[i - 1][j - 1] + 2 * image[i][j - 1] + image[i + 1][j - 1])
            image_new[i][j] = np.sqrt(np.square(sx) + np.square(sy))
    return image_new

三、Prewitt算子

与Sobel相比，Prewitt算子有一定的抗干扰性，图像效果比较干净。

公式如下：


################################################
#           Prewitt算子
################################################
def prewitt_filter(image):
    h = image.shape[0]
    w = image.shape[1]
    image_new = np.zeros(image.shape, np.uint8)
 
    for i in range(1, h-1):
        for j in range(1, w-1):
            sx = (image[i - 1][j - 1] + image[i - 1][j] + image[i - 1][j + 1]) - \
                 (image[i + 1][j - 1] + image[i + 1][j] + image[i + 1][j + 1])
            sy = (image[i - 1][j - 1] + image[i][j - 1] + image[i + 1][j - 1]) - \
                 (image[i - 1][j + 1] + image[i][j + 1] + image[i + 1][j + 1])
            image_new[i][j] = np.sqrt(np.square(sx) + np.square(sy))
    return image_new

四、Laplacian算子

拉普拉斯运算是偏导数运算的线性组合运算，属于二阶微分运算。与以上三类一阶微分运算相比，Laplacian算子获得的边界更为细致，包含了更多信息，

公式如下：


################################################
#           Laplacian算子
################################################
def laplacian_filter(image):
    h = image.shape[0]
    w = image.shape[1]
    image_new = np.zeros(image.shape, np.uint8)
    for i in range(1, h-1):
        for j in range(1, w-1):
            image_new[i][j] = image[i + 1][j] + image[i - 1][j] + image[i][j + 1] + image[i][j - 1] - 8 * image[i][j]
    return image_new

五、完整代码


import numpy as np
import matplotlib.pyplot as plt
import random
 
 
################################################
#           Robert算子
################################################
def robert_filter(image):
    h = image.shape[0]
    w = image.shape[1]
    image_new = np.zeros(image.shape, np.uint8)
    for i in range(1, h-1):
        for j in range(1, w-1):
            image_new[i][j] = np.abs((image[i][j]-image[i+1][j+1])) + np.abs(image[i+1][j]-image[i][j+1])
    return image_new
 
 
################################################
#           Sobel算子
################################################
def sobel_filter(image):
    h = image.shape[0]
    w = image.shape[1]
    image_new = np.zeros(image.shape, np.uint8)
 
    for i in range(1, h-1):
        for j in range(1, w-1):
            sx = (image[i + 1][j - 1] + 2 * image[i + 1][j] + image[i + 1][j + 1]) - \
                 (image[i - 1][j - 1] + 2 * image[i - 1][j] + image[i - 1][j + 1])
            sy = (image[i - 1][j + 1] + 2 * image[i][j + 1] + image[i + 1][j + 1]) - \
                 (image[i - 1][j - 1] + 2 * image[i][j - 1] + image[i + 1][j - 1])
            image_new[i][j] = np.sqrt(np.square(sx) + np.square(sy))
    return image_new
 
 
################################################
#           Prewitt算子
################################################
def prewitt_filter(image):
    h = image.shape[0]
    w = image.shape[1]
    image_new = np.zeros(image.shape, np.uint8)
 
    for i in range(1, h-1):
        for j in range(1, w-1):
            sx = (image[i - 1][j - 1] + image[i - 1][j] + image[i - 1][j + 1]) - \
                 (image[i + 1][j - 1] + image[i + 1][j] + image[i + 1][j + 1])
            sy = (image[i - 1][j - 1] + image[i][j - 1] + image[i + 1][j - 1]) - \
                 (image[i - 1][j + 1] + image[i][j + 1] + image[i + 1][j + 1])
            image_new[i][j] = np.sqrt(np.square(sx) + np.square(sy))
    return image_new
 
 
################################################
#           Laplacian算子
################################################
def laplacian_filter(image):
    h = image.shape[0]
    w = image.shape[1]
    image_new = np.zeros(image.shape, np.uint8)
    for i in range(1, h-1):
        for j in range(1, w-1):
            image_new[i][j] = image[i + 1][j] + image[i - 1][j] + image[i][j + 1] + image[i][j - 1] - 8 * image[i][j]
    return image_new
 
#############################################################################
 
 
if __name__ == "__main__":
    img = plt.imread("1.jpg")
 
    rgb_weight = [0.299, 0.587, 0.114]
    img_gray = np.dot(img, rgb_weight)
 
################################################
#           原图
################################################
    plt.subplot(241)
    plt.imshow(img)
    plt.xticks([])
    plt.yticks([])
    plt.title("Original")
 
################################################
#           灰度图
################################################
    plt.subplot(242)
    plt.imshow(img_gray, cmap=plt.cm.gray)
    plt.xticks([])
    plt.yticks([])
    plt.title("Gray")
 
################################################
#           Robert算子
################################################
    img_Robert = robert_filter(img_gray)
    img_Robert = img_Robert.astype(np.float64)
    plt.subplot(245)
    plt.imshow(img_Robert, cmap=plt.cm.gray)
    plt.xticks([])
    plt.yticks([])
    plt.title("robert_filter")
 
################################################
#           Sobel算子
################################################
    img_Sobel = sobel_filter(img_gray)
    img_Sobel = img_Sobel.astype(np.float64)
    plt.subplot(246)
    plt.imshow(img_Sobel, cmap=plt.cm.gray)
    plt.xticks([])
    plt.yticks([])
    plt.title("sobel_filter")
 
################################################
#           Prewitt算子
################################################
    img_Prewitt = prewitt_filter(img_gray)
    img_Prewitt = img_Prewitt.astype(np.float64)
    plt.subplot(247)
    plt.imshow(img_Prewitt, cmap=plt.cm.gray)
    plt.xticks([])
    plt.yticks([])
    plt.title("prewitt_filter")
 
################################################
#           Laplacian算子
################################################
    img_Laplacian = laplacian_filter(img_gray)
    img_Laplacian = img_Laplacian.astype(np.float64)
    plt.subplot(248)
    plt.imshow(img_Laplacian, cmap=plt.cm.gray)
    plt.xticks([])
    plt.yticks([])
    plt.title("laplacian_filter")
    plt.show()

结果如下：