python实现图像添加泊松噪声和高斯噪声

添加泊松噪声：

import numpy as np
import cv2

img = cv2.imread('image.jpg', 0)
lam = 10  # 控制噪声强度的参数
noise = np.random.poisson(lam, img.shape)
img_noise = np.uint8(np.clip(img + noise, 0, 255))

cv2.imshow('image', img)
cv2.imshow('noise', img_noise)
cv2.waitKey(0)
cv2.destroyAllWindows()

添加高斯噪声：

import numpy as np
import cv2

img = cv2.imread('image.jpg', 0)
mean = 0  # 噪声的均值
var = 100  # 噪声的方差
sigma = var ** 0.5  # 噪声的标准差
noise = np.random.normal(mean, sigma, img.shape)
img_noise = np.uint8(np.clip(img + noise, 0, 255))

cv2.imshow('image', img)
cv2.imshow('noise', img_noise)
cv2.waitKey(0)
cv2.destroyAllWindows()

相关问题

编写Python程序,给图像添加高斯噪声、椒盐噪声、泊松噪声、瑞利噪声、指数分布噪声和均匀分布噪声

可以使用OpenCV库来对图像添加噪声。下面是一个示例程序，展示如何给图像添加高斯噪声、椒盐噪声、泊松噪声、瑞利噪声、指数分布噪声和均匀分布噪声。

import cv2
import numpy as np

# 添加高斯噪声
def add_gaussian_noise(image):
    row, col, ch = image.shape
    mean = 0
    var = 0.1
    sigma = var ** 0.5
    gauss = np.random.normal(mean, sigma, (row, col, ch))
    gauss = gauss.reshape(row, col, ch)
    noisy_image = image + gauss
    return noisy_image

# 添加椒盐噪声
def add_salt_and_pepper_noise(image, ratio=0.05):
    row, col, ch = image.shape
    num_pixels = row * col
    num_salt = int(num_pixels * ratio)
    coords = [np.random.randint(0, i - 1, num_salt) for i in image.shape]
    image[coords] = 1

    num_pepper = int(num_pixels * ratio)
    coords = [np.random.randint(0, i - 1, num_pepper) for i in image.shape]
    image[coords] = 0
    return image

# 添加泊松噪声
def add_poisson_noise(image):
    vals = len(np.unique(image))
    vals = 2 ** np.ceil(np.log2(vals))
    noisy_image = np.random.poisson(image * vals) / float(vals)
    return noisy_image

# 添加瑞利噪声
def add_rayleigh_noise(image):
    row, col, ch = image.shape
    sigma = 0.1
    rayleigh = np.random.rayleigh(sigma, (row, col, ch))
    noisy_image = image + rayleigh
    return noisy_image

# 添加指数分布噪声
def add_exponential_noise(image):
    row, col, ch = image.shape
    scale = 0.1
    exponential = np.random.exponential(scale, (row, col, ch))
    noisy_image = image + exponential
    return noisy_image

# 添加均匀分布噪声
def add_uniform_noise(image):
    row, col, ch = image.shape
    low = -0.5
    high = 0.5
    uniform = np.random.uniform(low, high, (row, col, ch))
    noisy_image = image + uniform
    return noisy_image

# 读取图像
image_path = 'image.jpg'
image = cv2.imread(image_path)

# 添加高斯噪声
gaussian_noisy_image = add_gaussian_noise(image.copy())

# 添加椒盐噪声
salt_and_pepper_noisy_image = add_salt_and_pepper_noise(image.copy())

# 添加泊松噪声
poisson_noisy_image = add_poisson_noise(image.copy())

# 添加瑞利噪声
rayleigh_noisy_image = add_rayleigh_noise(image.copy())

# 添加指数分布噪声
exponential_noisy_image = add_exponential_noise(image.copy())

# 添加均匀分布噪声
uniform_noisy_image = add_uniform_noise(image.copy())

# 显示原图和添加噪声后的图像
cv2.imshow('Original Image', image)
cv2.imshow('Gaussian Noisy Image', gaussian_noisy_image)
cv2.imshow('Salt and Pepper Noisy Image', salt_and_pepper_noisy_image)
cv2.imshow('Poisson Noisy Image', poisson_noisy_image)
cv2.imshow('Rayleigh Noisy Image', rayleigh_noisy_image)
cv2.imshow('Exponential Noisy Image', exponential_noisy_image)
cv2.imshow('Uniform Noisy Image', uniform_noisy_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，这只是一个示例程序，你可以根据自己的需求进行修改和扩展。

编写程序，给图像添加高斯噪声、椒盐噪声、泊松噪声、瑞利噪声、指数分布噪声和均匀分布噪声。

您可以使用Python中的OpenCV库来给图像添加不同类型的噪声。下面是一个示例代码，演示如何添加高斯噪声、椒盐噪声、泊松噪声、瑞利噪声、指数分布噪声和均匀分布噪声。

import numpy as np
import cv2

def add_gaussian_noise(image, mean=0, stddev=10):
    noise = np.random.normal(mean, stddev, image.shape)
    noisy_image = np.clip(image + noise, 0, 255).astype(np.uint8)
    return noisy_image

def add_salt_pepper_noise(image, salt_prob=0.05, pepper_prob=0.05):
    noisy_image = np.copy(image)
    salt_coords = np.random.rand(*image.shape) < salt_prob
    pepper_coords = np.random.rand(*image.shape) < pepper_prob
    noisy_image[salt_coords] = 255
    noisy_image[pepper_coords] = 0
    return noisy_image

def add_poisson_noise(image):
    noise = np.random.poisson(image.astype(np.float32))
    noisy_image = np.clip(image + noise, 0, 255).astype(np.uint8)
    return noisy_image

def add_rayleigh_noise(image, scale=50):
    noise = np.random.rayleigh(scale, image.shape)
    noisy_image = np.clip(image + noise, 0, 255).astype(np.uint8)
    return noisy_image

def add_exponential_noise(image, scale=50):
    noise = np.random.exponential(scale, image.shape)
    noisy_image = np.clip(image + noise, 0, 255).astype(np.uint8)
    return noisy_image

def add_uniform_noise(image, low=0, high=50):
    noise = np.random.uniform(low, high, image.shape)
    noisy_image = np.clip(image + noise, 0, 255).astype(np.uint8)
    return noisy_image

# 读取图像
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# 添加高斯噪声
gaussian_noise_image = add_gaussian_noise(image)

# 添加椒盐噪声
salt_pepper_noise_image = add_salt_pepper_noise(image)

# 添加泊松噪声
poisson_noise_image = add_poisson_noise(image)

# 添加瑞利噪声
rayleigh_noise_image = add_rayleigh_noise(image)

# 添加指数分布噪声
exponential_noise_image = add_exponential_noise(image)

# 添加均匀分布噪声
uniform_noise_image = add_uniform_noise(image)

# 显示原始图像和添加噪声后的图像
cv2.imshow('Original Image', image)
cv2.imshow('Gaussian Noise Image', gaussian_noise_image)
cv2.imshow('Salt and Pepper Noise Image', salt_pepper_noise_image)
cv2.imshow('Poisson Noise Image', poisson_noise_image)
cv2.imshow('Rayleigh Noise Image', rayleigh_noise_image)
cv2.imshow('Exponential Noise Image', exponential_noise_image)
cv2.imshow('Uniform Noise Image', uniform_noise_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

请确保将代码中的'image.jpg'替换为您想要添加噪声的图像的路径。此代码将在窗口中显示原始图像和添加噪声后的图像。您可以根据需要调整噪声参数。