计算机视觉：Opencv图像去噪

本博客针对某一原始图片添加高斯或椒盐噪声，再使用均值、中值、高斯和双边滤波对加噪图像进行去噪，相关函数如下所示。

添加高斯噪声

def clamp(pv):
    if pv > 255:
        return 255
    if pv < 0:
        return 0
    else:
        return pv

def gaussian_noise(image): 
    image_ori = image.copy()
    
    h, w, c = image.shape
    for row in range(h):
        for col in range(w):
            s = np.random.normal(0, 20, 3)
            # s = np.random.normal(0, 60, 3)
            b = image[row, col, 0] # blue
            g = image[row, col, 1] # green
            r = image[row, col, 2] # red
            image[row, col, 0] = clamp(b + s[0])
            image[row, col, 1] = clamp(g + s[1])
            image[row, col, 2] = clamp(r + s[2])
    cv2.imwrite('gaussian_noise_picture.png', image)
    return image

添加椒盐噪声

def sp_noise(image):
    prob = 0.01
    output = np.zeros(image.shape,np.uint8)
    thres = 1 - prob
    for i in range(image.shape[0]):
        for j in range(image.shape[1]):
            rdn = random.random()
            if rdn < prob:
                output[i][j] = 0
            elif rdn > thres:
                output[i][j] = 255
            else:
                output[i][j] = image[i][j]
	cv2.imwrite('sp_noise_picture.png',output)
    return output

均值滤波

def mean_filter(noise_img):
    result = cv2.blur(noise_img, (5, 5))
    cv2.imwrite('mean_filter_picture.png', result)

中值滤波

def median_filter(noise_img):
    result = cv2.medianBlur(noise_img, 5)
    cv2.imwrite("median_filter_picture.png", result)

高斯滤波

def gaussian_filter(noise_img):
    gaussian_blurred = cv2.GaussianBlur(noise_img, (5, 5), 0)
    cv2.imwrite('gaussian_filter_picture.png', gaussian_blurred)

双边滤波

def bilateral_filter(noise_img):
    bilateral_blurred = cv2.bilateralFilter(noise_img, d=20, sigmaColor=50, sigmaSpace=15)
    cv2.imwrite('bilateral_filter_picture.png', bilateral_blurred)