.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/decomposition/plot_image_denoising.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_decomposition_plot_image_denoising.py: ========================================= 使用字典学习进行图像去噪 ========================================= 一个比较示例，展示了使用在线 :ref:`DictionaryLearning` 和各种变换方法重建浣熊脸图像的噪声片段的效果。 字典在图像的左半部分失真区域上进行拟合，随后用于重建右半部分。请注意，通过拟合无失真（即无噪声）图像可以获得更好的性能，但这里我们假设无失真图像不可用。 评估图像去噪结果的常见做法是查看重建图像与原始图像之间的差异。如果重建是完美的，这种差异将看起来像高斯噪声。 从图表中可以看出，使用两个非零系数的 :ref:`omp` 结果比仅保留一个系数的结果偏差更小（边缘看起来不那么明显）。此外，在 Frobenius 范数下，它更接近真实值。 :ref:`least_angle_regression` 的结果偏差更大：差异让人联想到原始图像的局部强度值。 阈值处理显然对去噪没有用，但这里展示它可以以非常高的速度产生有启发性的输出，因此在性能不一定与可视化相关的其他任务（如对象分类）中可能有用。 .. GENERATED FROM PYTHON SOURCE LINES 21-23 生成失真图像 ------------------------ .. GENERATED FROM PYTHON SOURCE LINES 23-52 .. code-block:: Python import numpy as np try: # Scipy >= 1.10 from scipy.datasets import face except ImportError: from scipy.misc import face raccoon_face = face(gray=True) # 将值在0到255之间的uint8表示转换为值在0到1之间的浮点表示。 raccoon_face = raccoon_face / 255.0 # 降采样以提高速度 raccoon_face = ( raccoon_face[ : :4, ::4] + raccoon_face[1 : :4, ::4] + raccoon_face[ : :4, 1::4] + raccoon_face[1 : :4, 1::4] ) raccoon_face /= 4.0 height, width = raccoon_face.shape # 扭曲图像的右半部分 print("Distorting image...") distorted = raccoon_face.copy() distorted[:, width // 2 :] += 0.075 * np.random.randn(height, width // 2) .. rst-class:: sphx-glr-script-out .. code-block:: none Distorting image... .. GENERATED FROM PYTHON SOURCE LINES 53-55 显示失真图像 --------------------------- .. GENERATED FROM PYTHON SOURCE LINES 55-83 .. code-block:: Python import matplotlib.pyplot as plt def show_with_diff(image, reference, title): """辅助函数用于显示去噪""" plt.figure(figsize=(5, 3.3)) plt.subplot(1, 2, 1) plt.title("Image") plt.imshow(image, vmin=0, vmax=1, cmap=plt.cm.gray, interpolation="nearest") plt.xticks(()) plt.yticks(()) plt.subplot(1, 2, 2) difference = image - reference plt.title("Difference (norm: %.2f)" % np.sqrt(np.sum(difference**2))) plt.imshow( difference, vmin=-0.5, vmax=0.5, cmap=plt.cm.PuOr, interpolation="nearest" ) plt.xticks(()) plt.yticks(()) plt.suptitle(title, size=16) plt.subplots_adjust(0.02, 0.02, 0.98, 0.79, 0.02, 0.2) show_with_diff(distorted, raccoon_face, "Distorted image") .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_001.png :alt: Distorted image, Image, Difference (norm: 11.71) :srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 84-86 提取参考补丁 ---------------------------- .. GENERATED FROM PYTHON SOURCE LINES 86-102 .. code-block:: Python from time import time from sklearn.feature_extraction.image import extract_patches_2d # 从图像的左半部分提取所有参考块 print("Extracting reference patches...") t0 = time() patch_size = (7, 7) data = extract_patches_2d(distorted[:, : width // 2], patch_size) data = data.reshape(data.shape[0], -1) data -= np.mean(data, axis=0) data /= np.std(data, axis=0) print(f"{data.shape[0]} patches extracted in %.2fs." % (time() - t0)) .. rst-class:: sphx-glr-script-out .. code-block:: none Extracting reference patches... 22692 patches extracted in 0.00s. .. GENERATED FROM PYTHON SOURCE LINES 103-105 从参考图像块中学习字典 ------------------------- .. GENERATED FROM PYTHON SOURCE LINES 105-135 .. code-block:: Python from sklearn.decomposition import MiniBatchDictionaryLearning print("Learning the dictionary...") t0 = time() dico = MiniBatchDictionaryLearning( # 增加到300以获得更高质量的结果，但训练时间会变慢。 n_components=50, batch_size=200, alpha=1.0, max_iter=10, ) V = dico.fit(data).components_ dt = time() - t0 print(f"{dico.n_iter_} iterations / {dico.n_steps_} steps in {dt:.2f}.") plt.figure(figsize=(4.2, 4)) for i, comp in enumerate(V[:100]): plt.subplot(10, 10, i + 1) plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r, interpolation="nearest") plt.xticks(()) plt.yticks(()) plt.suptitle( "Dictionary learned from face patches\n" + "Train time %.1fs on %d patches" % (dt, len(data)), fontsize=16, ) plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23) .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_002.png :alt: Dictionary learned from face patches Train time 6.6s on 22692 patches :srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Learning the dictionary... 2.0 iterations / 118 steps in 6.62. .. GENERATED FROM PYTHON SOURCE LINES 136-138 提取噪声补丁并使用字典重建它们 --------------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 138-178 .. code-block:: Python from sklearn.feature_extraction.image import reconstruct_from_patches_2d print("Extracting noisy patches... ") t0 = time() data = extract_patches_2d(distorted[:, width // 2 :], patch_size) data = data.reshape(data.shape[0], -1) intercept = np.mean(data, axis=0) data -= intercept print("done in %.2fs." % (time() - t0)) transform_algorithms = [ ("Orthogonal Matching Pursuit\n1 atom", "omp", {"transform_n_nonzero_coefs": 1}), ("Orthogonal Matching Pursuit\n2 atoms", "omp", {"transform_n_nonzero_coefs": 2}), ("Least-angle regression\n4 atoms", "lars", {"transform_n_nonzero_coefs": 4}), ("Thresholding\n alpha=0.1", "threshold", {"transform_alpha": 0.1}), ] reconstructions = {} for title, transform_algorithm, kwargs in transform_algorithms: print(title + "...") reconstructions[title] = raccoon_face.copy() t0 = time() dico.set_params(transform_algorithm=transform_algorithm, **kwargs) code = dico.transform(data) patches = np.dot(code, V) patches += intercept patches = patches.reshape(len(data), *patch_size) if transform_algorithm == "threshold": patches -= patches.min() patches /= patches.max() reconstructions[title][:, width // 2 :] = reconstruct_from_patches_2d( patches, (height, width // 2) ) dt = time() - t0 print("done in %.2fs." % dt) show_with_diff(reconstructions[title], raccoon_face, title + " (time: %.1fs)" % dt) plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_003.png :alt: Orthogonal Matching Pursuit 1 atom (time: 0.3s), Image, Difference (norm: 10.73) :srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_004.png :alt: Orthogonal Matching Pursuit 2 atoms (time: 0.5s), Image, Difference (norm: 9.36) :srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_004.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_005.png :alt: Least-angle regression 4 atoms (time: 3.5s), Image, Difference (norm: 13.23) :srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_005.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_006.png :alt: Thresholding alpha=0.1 (time: 0.1s), Image, Difference (norm: 14.29) :srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_006.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none Extracting noisy patches... done in 0.00s. Orthogonal Matching Pursuit 1 atom... done in 0.26s. Orthogonal Matching Pursuit 2 atoms... done in 0.47s. Least-angle regression 4 atoms... done in 3.50s. Thresholding alpha=0.1... done in 0.06s. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 11.675 seconds) .. _sphx_glr_download_auto_examples_decomposition_plot_image_denoising.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/decomposition/plot_image_denoising.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_image_denoising.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_image_denoising.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_image_denoising.zip ` .. include:: plot_image_denoising.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_