Add salt and pepper noise or Gaussian noise to the picture

Add salt and pepper noise or Gaussian noise to the picture

Salt and pepper noise added

Salt and pepper noise （salt & pepper noise） Is a common noise in digital images , Salt and pepper , Pepper is black , Salt is white , Salt and pepper noise is the random appearance of black and white pixels on the image . Salt and pepper noise is a kind of noise caused by signal pulse strength , The algorithm to generate this noise is also relatively simple .

Add salt and pepper noise to the grayscale image

# Add salt and pepper noise to grayscale images 
# src  Enter the path of the image 
# dst  Output image path 
# prob  Proportion of noise 
def addSaltPepperNoise_gray(src,dst,probility=0.05,method="salt_pepper"):
    # Convert the picture into a grayscale image 
    imarray=np.array(Image.open(src).convert('L'))
    height,width=imarray.shape

    for i in range(height):
        for j in range(width):
            # Add salt and pepper noise randomly 
            if np.random.random(1)<probility:
                if np.random.random(1)<0.05:
                    imarray[i,j]=0
                else:
                    imarray[i,j]=255
    # Add the generated salt and pepper noise image to 
    new_im=Image.fromarray(imarray)
    new_im.save(dst)
    return new_im

Add salt and pepper noise to the color map

def RGBAddNoise(src, dst, prob):  #  Add clutter at the same time (RGB Single noise )RGB Figure noise  prob: Noise ratio 

    imarray = np.array(Image.open(src))
    height, width, channels = imarray.shape

    # prob = 0.05 # Noise ratio   It's already obvious  >0.1  Seriously affect the image quality 
    NoiseImg = imarray.copy()
    NoiseNum = int(prob * height * width)
    for i in range(NoiseNum):
        rows = np.random.randint(0, height - 1)
        cols = np.random.randint(0, width - 1)
        channel = np.random.randint(0, 3)
        if np.random.randint(0, 2) == 0:
            NoiseImg[rows, cols, channel] = 0
        else:
            NoiseImg[rows, cols, channel] = 255
    # return NoiseImg
    new_im = Image.fromarray(NoiseImg)
    new_im.save(dst)
    return new_im

Gaussian noise added

Gaussian noise means that the probability density function of noise distribution obeys Gaussian distribution （ Normal distribution ） A kind of noise , The main reason is that the camera has a dark field of view and uneven brightness when shooting , At the same time, high temperature caused by long-time operation of the camera will also cause Gaussian noise , In addition, the noise and mutual influence of circuit components are also one of the important reasons for Gaussian noise .

Add Gaussian noise to the grayscale image

def addGaussNoiseGray(src,dst,mean,sigma):
    image=cv2.imread(src,cv2.IMREAD_GRAYSCALE)
    height,width=image.shape
    gauss=np.random.normal(mean,sigma,image.shape)
    gauss=gauss.reshape(height,width)
    noiseImage=image+gauss
    noiseImage=np.clip(noiseImage,0,255)
    noiseImage=noiseImage.astype('uint8')

    cv2.imwrite(dst,noiseImage)
    return noiseImage

Add Gaussian noise to the color map

# Add Gaussian noise to color images 
def addGaussNoiseRgb(src,dst,mean,sigma):
    image=cv2.imread(src)
    height,width,channels=image.shape

    # Establish Gaussian noise 
    gauss=np.random.normal(mean,sigma,(height,width,channels))
    gauss=gauss.reshape(height,width,channels)
    noiseImage=image+gauss

    noiseImage=np.clip(noiseImage,0,255)
    noiseImage=noiseImage.astype('uint8')

    cv2.imwrite(dst,noiseImage)
    return noiseImage

Result display