由于您没有提供任何输入数据，因此我从here提取了灰度图像，并使用 ImageMagick 按GBRG的顺序将其制成了原始Bayer8文件：

magick mandi.png -trim -depth 8 gray:bayer.bin

这为我提供了680,736字节的1013x672像素文件。

然后我像这样阅读它，并将其制作为skimage可以理解的图像：

#!/usr/bin/env python3

import numpy as np
from skimage.io import imsave

# Width and height of Bayer image,not original which is w/2 x h/2
w,h = 1013,672
ow,oh = w//2,h//2

# Load in Bayer8 image,assumed raw 8-bit GBRG
bayer = np.fromfile('bayer.bin',dtype=np.uint8).reshape((h,w))

# Pick up raw uint8 samples
R  = bayer[1::2,0::2]     # rows 1,3,5,7 columns 0,2,4,6
B  = bayer[0::2,1::2]     # rows 0,6 columns 1,7
G0 = bayer[0::2,0::2]     # rows 0,6 columns 0,6
G1 = bayer[1::2,1::2]     # rows 1,7 columns 1,7

# Chop any left-over edges and average the 2 Green values
R = R[:oh,:ow]
B = B[:oh,:ow]
G = G0[:oh,:ow]//2 + G1[:oh,:ow]//2

# Formulate image by stacking R,G and B and save
out = np.dstack((R,G,B)) 
imsave('result.png',out)

并得到这个：

版权Mathworks，Inc。

当然，还有更复杂的插值方法，但这是最基本的插值方法，欢迎您使用它并加以改进！

好的，我花了一些时间，尝试对Bayer数组中的缺失值进行2D插值。我对答案没有100％的信心，但我认为它应该非常接近。

基本上，我以全分辨率复制原始的Bayer数组，并用np.Nan覆盖所有绿色和蓝色样本，并将其称为Red。然后我进行二维插值替换Nans。

再次与绿色和蓝色相同，这样：

#!/usr/bin/env python3

import numpy as np
from skimage.io import imsave
from scipy.interpolate import griddata

def interp2d(im):
    """Interpolate in 2d array,replacing NaNs with interpolated values"""
    x,y = np.indices(im.shape)
    im[np.isnan(im)] = griddata(
       (x[~np.isnan(im)],y[~np.isnan(im)]),im[~np.isnan(im)],(x[np.isnan(im)],y[np.isnan(im)]))
    im = np.nan_to_num(im)
    return np.clip((im),255)

# Width and height of Bayer image
w,672

# Calculate output width and height as multiples of 4
ow = (w//4) * 4
oh = (h//4) * 4

# Load in Bayer8 image,assumed raw 8-bit GBRG,reshape and make sides multiple of 4
bayer = np.fromfile('bayer.bin',w)).astype(np.float)[:oh,:ow]

# In following code you'll see "cell" which is the basic repeating 2x2 cell of a Bayer matrix
#
# cell = G B
#        R G
#

# Set everything not Red in bayer array to Nan,then replace Nans with interpolation
cell = np.array([[np.NaN,np.NaN],[1.0,np.NaN]])
R = bayer*np.tile(cell,(oh//2,ow//2))
R = interp2d(R).astype(np.uint8)

# Set everything not Green in bayer array to Nan,then replace Nans with interpolation
cell = np.array([[1.0,[np.NaN,1.0   ]])
G = bayer*np.tile(cell,ow//2))
G = interp2d(G).astype(np.uint8)

# Set everything not Blue in bayer array to Nan,1.0   ],np.NaN]])
B = bayer*np.tile(cell,ow//2))
B = interp2d(B).astype(np.uint8)

# Form image by stacking R,G and B and save
imsave('result.png',np.dstack((R,B)))

关键字：Python，Bayer，bayer8，debayer，de-bayer，de-mosaic，de-mosaicking，image，raw，CFA，skimage，scikit-image，图像处理。