python - 一维阵列的Richardson-Lucy反卷积

我正在寻找一种Richardson-Lucy反卷积算法的实现，该算法可用于一维数组，例如光谱数据。我尝试了scikit-image，但显然它仅适用于图像。

最佳答案

您是否尝试过在单行/单列二维数组上使用restoration.richardson_lucy？它能按预期工作吗？

这是基于http://scikit-image.org/docs/dev/auto_examples/filters/plot_deconvolution.html的示例（请参见输入单元3和4）：

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  ])

如果您发现转换为2D确实很不方便，请随时在GitHub上提出问题。

关于python - 一维阵列的Richardson-Lucy反卷积，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/48440907/