自动从python中的图像中删除热/死像素

Question

我正在使用 numpy 和 scipy 来处理使用 CCD 相机拍摄的大量图像。这些图像具有许多具有非常大（或小）值的热（和死）像素。这些会干扰其他图像处理，因此需要将其删除。不幸的是，尽管一些像素停留在 0 或 255 并且在所有图像中始终处于相同的值，但仍有一些像素在几分钟内暂时停留在其他值（数据跨越很长时间）。

我想知道是否有一种方法可以识别（和删除）已经在 python 中实现的热点像素。如果没有，我想知道这样做的有效方法是什么。通过与相邻像素进行比较，热/死像素相对容易识别。我可以看到编写一个循环来查看每个像素，将其值与其 8 个最近邻居的值进行比较。或者，使用某种卷积来生成更平滑的图像，然后从包含热像素的图像中减去它似乎更好，使它们更容易识别。

我已经在下面的代码中尝试过这种“模糊方法”，它工作正常，但我怀疑它是最快的。此外，它在图像的边缘会混淆（可能是因为 gaussian_filter 函数正在进行卷积并且卷积在边缘附近变得奇怪）。那么，有没有更好的方法来解决这个问题？

示例代码：

import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage

plt.figure(figsize=(8,4))
ax1 = plt.subplot(121)
ax2 = plt.subplot(122)

#make a sample image
x = np.linspace(-5,5,200)
X,Y = np.meshgrid(x,x)
Z = 255*np.cos(np.sqrt(x**2 + Y**2))**2


for i in range(0,11):
    #Add some hot pixels
    Z[np.random.randint(low=0,high=199),np.random.randint(low=0,high=199)]= np.random.randint(low=200,high=255)
    #and dead pixels
    Z[np.random.randint(low=0,high=199),np.random.randint(low=0,high=199)]= np.random.randint(low=0,high=10)

#Then plot it
ax1.set_title('Raw data with hot pixels')
ax1.imshow(Z,interpolation='nearest',origin='lower')

#Now we try to find the hot pixels
blurred_Z = scipy.ndimage.gaussian_filter(Z, sigma=2)
difference = Z - blurred_Z

ax2.set_title('Difference with hot pixels identified')
ax2.imshow(difference,interpolation='nearest',origin='lower')

threshold = 15
hot_pixels = np.nonzero((difference>threshold) | (difference<-threshold))

#Don't include the hot pixels that we found near the edge:
count = 0
for y,x in zip(hot_pixels[0],hot_pixels[1]):
    if (x != 0) and (x != 199) and (y != 0) and (y != 199):
        ax2.plot(x,y,'ro')
        count += 1

print 'Detected %i hot/dead pixels out of 20.'%count
ax2.set_xlim(0,200); ax2.set_ylim(0,200)


plt.show()

然后输出：

Answer 1

基本上，我认为处理热点像素的最快方法就是使用 size=2 中值滤波器。然后，噗，您的热像素消失了，您还消除了相机中的各种其他高频传感器噪声。

如果您真的只想删除热像素，那么您可以像我在问题中所做的那样，从原始图像中减去中值滤波器，然后仅将这些值替换为中值滤波图像中的值。这在边缘效果不好，所以如果你可以忽略边缘的像素，那么这会让事情变得容易很多。

如果你想处理边缘，你可以使用下面的代码。但是，它并不是最快的：

import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage

plt.figure(figsize=(10,5))
ax1 = plt.subplot(121)
ax2 = plt.subplot(122)

#make some sample data
x = np.linspace(-5,5,200)
X,Y = np.meshgrid(x,x)
Z = 100*np.cos(np.sqrt(x**2 + Y**2))**2 + 50

np.random.seed(1)
for i in range(0,11):
    #Add some hot pixels
    Z[np.random.randint(low=0,high=199),np.random.randint(low=0,high=199)]= np.random.randint(low=200,high=255)
    #and dead pixels
    Z[np.random.randint(low=0,high=199),np.random.randint(low=0,high=199)]= np.random.randint(low=0,high=10)

#And some hot pixels in the corners and edges
Z[0,0]   =255
Z[-1,-1] =255
Z[-1,0]  =255
Z[0,-1]  =255
Z[0,100] =255
Z[-1,100]=255
Z[100,0] =255
Z[100,-1]=255

#Then plot it
ax1.set_title('Raw data with hot pixels')
ax1.imshow(Z,interpolation='nearest',origin='lower')

def find_outlier_pixels(data,tolerance=3,worry_about_edges=True):
    #This function finds the hot or dead pixels in a 2D dataset. 
    #tolerance is the number of standard deviations used to cutoff the hot pixels
    #If you want to ignore the edges and greatly speed up the code, then set
    #worry_about_edges to False.
    #
    #The function returns a list of hot pixels and also an image with with hot pixels removed

    from scipy.ndimage import median_filter
    blurred = median_filter(Z, size=2)
    difference = data - blurred
    threshold = 10*np.std(difference)

    #find the hot pixels, but ignore the edges
    hot_pixels = np.nonzero((np.abs(difference[1:-1,1:-1])>threshold) )
    hot_pixels = np.array(hot_pixels) + 1 #because we ignored the first row and first column

    fixed_image = np.copy(data) #This is the image with the hot pixels removed
    for y,x in zip(hot_pixels[0],hot_pixels[1]):
        fixed_image[y,x]=blurred[y,x]

    if worry_about_edges == True:
        height,width = np.shape(data)

        ###Now get the pixels on the edges (but not the corners)###

        #left and right sides
        for index in range(1,height-1):
            #left side:
            med  = np.median(data[index-1:index+2,0:2])
            diff = np.abs(data[index,0] - med)
            if diff>threshold: 
                hot_pixels = np.hstack(( hot_pixels, [[index],[0]]  ))
                fixed_image[index,0] = med

            #right side:
            med  = np.median(data[index-1:index+2,-2:])
            diff = np.abs(data[index,-1] - med)
            if diff>threshold: 
                hot_pixels = np.hstack(( hot_pixels, [[index],[width-1]]  ))
                fixed_image[index,-1] = med

        #Then the top and bottom
        for index in range(1,width-1):
            #bottom:
            med  = np.median(data[0:2,index-1:index+2])
            diff = np.abs(data[0,index] - med)
            if diff>threshold: 
                hot_pixels = np.hstack(( hot_pixels, [[0],[index]]  ))
                fixed_image[0,index] = med

            #top:
            med  = np.median(data[-2:,index-1:index+2])
            diff = np.abs(data[-1,index] - med)
            if diff>threshold: 
                hot_pixels = np.hstack(( hot_pixels, [[height-1],[index]]  ))
                fixed_image[-1,index] = med

        ###Then the corners###

        #bottom left
        med  = np.median(data[0:2,0:2])
        diff = np.abs(data[0,0] - med)
        if diff>threshold: 
            hot_pixels = np.hstack(( hot_pixels, [[0],[0]]  ))
            fixed_image[0,0] = med

        #bottom right
        med  = np.median(data[0:2,-2:])
        diff = np.abs(data[0,-1] - med)
        if diff>threshold: 
            hot_pixels = np.hstack(( hot_pixels, [[0],[width-1]]  ))
            fixed_image[0,-1] = med

        #top left
        med  = np.median(data[-2:,0:2])
        diff = np.abs(data[-1,0] - med)
        if diff>threshold: 
            hot_pixels = np.hstack(( hot_pixels, [[height-1],[0]]  ))
            fixed_image[-1,0] = med

        #top right
        med  = np.median(data[-2:,-2:])
        diff = np.abs(data[-1,-1] - med)
        if diff>threshold: 
            hot_pixels = np.hstack(( hot_pixels, [[height-1],[width-1]]  ))
            fixed_image[-1,-1] = med

    return hot_pixels,fixed_image


hot_pixels,fixed_image = find_outlier_pixels(Z)

for y,x in zip(hot_pixels[0],hot_pixels[1]):
    ax1.plot(x,y,'ro',mfc='none',mec='r',ms=10)

ax1.set_xlim(0,200)
ax1.set_ylim(0,200)

ax2.set_title('Image with hot pixels removed')
ax2.imshow(fixed_image,interpolation='nearest',origin='lower',clim=(0,255))

plt.show()

输出：