mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 03:51:05 +08:00
Samuel St-Jean
a908201872
Seems like the dynamic range values got swapped, the noise image goes up to 0.14 with the correct numbers.
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
"""
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===========================
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Structural similarity index
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===========================
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When comparing images, the mean squared error (MSE)--while simple to
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implement--is not highly indicative of perceived similarity. Structural
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similarity aims to address this shortcoming by taking texture into account
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[1]_, [2]_.
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The example shows two modifications of the input image, each with the same MSE,
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but with very different mean structural similarity indices.
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.. [1] Zhou Wang; Bovik, A.C.; ,"Mean squared error: Love it or leave it? A new
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look at Signal Fidelity Measures," Signal Processing Magazine, IEEE,
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vol. 26, no. 1, pp. 98-117, Jan. 2009.
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.. [2] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality
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assessment: From error visibility to structural similarity," IEEE
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Transactions on Image Processing, vol. 13, no. 4, pp. 600-612,
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Apr. 2004.
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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 data, img_as_float
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from skimage.measure import compare_ssim as ssim
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img = img_as_float(data.camera())
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rows, cols = img.shape
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noise = np.ones_like(img) * 0.2 * (img.max() - img.min())
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noise[np.random.random(size=noise.shape) > 0.5] *= -1
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def mse(x, y):
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return np.linalg.norm(x - y)
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img_noise = img + noise
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img_const = img + abs(noise)
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fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(16, 6),
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sharex=True, sharey=True,
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subplot_kw={'adjustable': 'box-forced'})
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plt.tight_layout()
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mse_none = mse(img, img)
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ssim_none = ssim(img, img, dynamic_range=img.max() - img.min())
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mse_noise = mse(img, img_noise)
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ssim_noise = ssim(img, img_noise,
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dynamic_range=img_noise.max() - img_noise.min())
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mse_const = mse(img, img_const)
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ssim_const = ssim(img, img_const,
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dynamic_range=img_const.max() - img_const.min())
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label = 'MSE: %2.f, SSIM: %.2f'
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ax0.imshow(img, cmap=plt.cm.gray, vmin=0, vmax=1)
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ax0.set_xlabel(label % (mse_none, ssim_none))
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ax0.set_title('Original image')
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ax0.axes.get_yaxis().set_visible(False)
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ax1.imshow(img_noise, cmap=plt.cm.gray, vmin=0, vmax=1)
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ax1.set_xlabel(label % (mse_noise, ssim_noise))
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ax1.set_title('Image with noise')
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ax1.axes.get_yaxis().set_visible(False)
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ax2.imshow(img_const, cmap=plt.cm.gray, vmin=0, vmax=1)
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ax2.set_xlabel(label % (mse_const, ssim_const))
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ax2.set_title('Image plus constant')
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ax2.axes.get_yaxis().set_visible(False)
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plt.show()
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