Python 一维阵列的Richardson-Lucy反褶积

我正在寻找Richardson-Lucy反褶积算法的实现，该算法适用于一维阵列，如光谱数据。我试过了，但显然它只适用于图像。

你试过在一行/一列2D数组上使用

restoration.richardson\u lucy

吗？它是否按预期工作

下面是一个基于的示例（请参见输入单元格3和4）：

如果您觉得2D转换非常不方便，可以在GitHub上提出问题

In [1]: import numpy as np
   ...: import matplotlib.pyplot as plt
   ...: 
   ...: from scipy.signal import convolve2d as conv2
   ...: 
   ...: from skimage import color, data, restoration
   ...: 
   ...: astro = color.rgb2gray(data.astronaut())
   ...: 

In [2]: 
   ...: psf = np.ones((5, 5)) / 25
   ...: astro = conv2(astro, psf, 'same')
   ...: # Add Noise to Image
   ...: astro_noisy = astro.copy()
   ...: astro_noisy += (np.random.poisson(lam=25, size=astro.shape) - 10) / 255.
   ...: 
   ...: 

In [3]: astro_1d = astro[:1, :]
In [4]: psf_1d = psf[:1, :] * 5

In [5]: deconvolved_RL = restoration.richardson_lucy(astro_1d, psf_1d, iteration
   ...: s=30)
   ...: 
   ...: 

In [8]: deconvolved_RL[0][:10]
Out[8]: 
array([  3.68349589e-06,   4.64232976e-03,   8.96492325e-01,
         2.92227252e-01,   2.27669473e-01,   1.63909318e-01,
         2.62231088e-01,   5.63304220e-01,   4.29589937e-01,
         3.21857292e-01])

In [9]: astro_1d[0][:10]
Out[9]: 
array([ 0.20156543,  0.25178911,  0.31006612,  0.29581576,  0.30208733,
        0.32490093,  0.35101666,  0.36213184,  0.35174074,  0.318339  ])