Laplacian滤波器实现并用于图像锐化

一. 拉普拉斯滤波器简介：

        我们知道：

二. 3*3的laplacian滤波器实现

# laplacian filter

def laplacian_filter(img, K_size=3):

    H, W = img.shape

    # zero padding

    pad = K_size // 2

    out = np.zeros((H + pad * 2, W + pad * 2), dtype=np.float)

    out[pad: pad + H, pad: pad + W] = img.copy().astype(np.float)

    tmp = out.copy()

    # laplacian kernle

    K = [[0., 1., 0.],[1., -4., 1.], [0., 1., 0.]]

    # filtering

    for y in range(H):

        for x in range(W):

            out[pad + y, pad + x] = np.sum(K * (tmp[y: y + K_size, x: x + K_size]))

    out = np.clip(out, 0, 255)

    out = out[pad: pad + H, pad: pad + W].astype(np.uint8)

    return out

三. 利用laplacian滤波器实现图像的锐化

        由于拉普拉斯是一种微分算子，它的应用可增强图像中灰度突变的区域，减弱灰度的缓慢变化区域。

        因此，锐化处理可选择拉普拉斯算子对原图像进行处理，产生描述灰度突变的图像，再将拉普拉斯图像与原始图像叠加而产生锐化图像：

        其中，f(x,y)为原始图像，g(x,y)为锐化后图像，c为-1（卷积核中间为负数时，若卷积核中间为正数，则c为1）。

四. 通过laplacian滤波器实现图像锐化 python源码

import cv2

import numpy as np

# Image sharpening by laplacian filter

def laplacian_sharpening(img, K_size=3):

    H, W = img.shape

    # zero padding

    pad = K_size // 2

    out = np.zeros((H + pad * 2, W + pad * 2), dtype=np.float)

    out[pad: pad + H, pad: pad + W] = img.copy().astype(np.float)

    tmp = out.copy()

    # laplacian kernle

    K = [[0., 1., 0.],[1., -4., 1.], [0., 1., 0.]]

    # filtering and adding image -> Sharpening image

    for y in range(H):

        for x in range(W):

            # core code

            out[pad + y, pad + x] = (-1) * np.sum(K * (tmp[y: y + K_size, x: x + K_size])) + tmp[pad + y, pad + x]

    out = np.clip(out, 0, 255)

    out = out[pad: pad + H, pad: pad + W].astype(np.uint8)

    return out

# Read Gray Scale image

img = cv2.imread("../paojie_g.jpg",0).astype(np.float)

# Image sharpening by laplacian filter

out = laplacian_sharpening(img, K_size=3)

# Save result

cv2.imwrite("out.jpg", out)

cv2.imshow("result", out)

cv2.waitKey(0)

cv2.destroyAllWindows()

五. 实验结果：

六. 参考内容