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François Orieux
59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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=====================
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Deconvolution of Lena
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=====================
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In this example, we deconvolve a noisy version of Lena using wiener
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and unsupervised wiener algorithms. This algorithms are known to not
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have the best respect of sharp edge in the image.
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Wiener filter
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-------------
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This is simply the inverse filter based on the PSF, the prior
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regularisation (penalisation of high frequency) and the tradeoff
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between the data and prior adequacy. The regularization parameter must
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be hand tuned.
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Unsupervised wiener
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-------------------
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This algorithm has a self tuned regularisation parameters based on
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data learning. This is not common and based on the following publication
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.. [1] François Orieux, Jean-François Giovannelli, and Thomas
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Rodet, "Bayesian estimation of regularization and point
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spread function parameters for Wiener-Hunt deconvolution",
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J. Opt. Soc. Am. A 27, 1593-1607 (2010)
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage import color, data, deconvolution
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lena = color.rgb2gray(data.lena())
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from scipy.signal import convolve2d as conv2
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psf = np.ones((5, 5))
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lena = conv2(lena, psf, 'same')
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lena += 0.1 * lena.std() * np.random.standard_normal(lena.shape)
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deconvolued, _ = deconvolution.unsupervised_wiener(lena, psf)
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fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))
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plt.gray()
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ax[0].imshow(lena)
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ax[0].axis('off')
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ax[0].set_title('Data')
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ax[1].imshow(deconvolued, vmax=lena.max())
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ax[1].axis('off')
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ax[1].set_title('Self tuned deconvolution')
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fig.subplots_adjust(wspace=0.02, hspace=0.2,
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top=0.9, bottom=0.05, left=0, right=1)
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plt.show()
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