.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_tomography_l1_reconstruction.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 JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py: ====================================================================== Compressive sensing: tomography reconstruction with L1 prior (Lasso) ====================================================================== This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in **computed tomography** (CT). Without any prior information on the sample, the number of projections required to reconstruct the image is of the order of the linear size ``l`` of the image (in pixels). For simplicity we consider here a sparse image, where only pixels on the boundary of objects have a non-zero value. Such data could correspond for example to a cellular material. Note however that most images are sparse in a different basis, such as the Haar wavelets. Only ``l/7`` projections are acquired, therefore it is necessary to use prior information available on the sample (its sparsity): this is an example of **compressive sensing**. The tomography projection operation is a linear transformation. In addition to the data-fidelity term corresponding to a linear regression, we penalize the L1 norm of the image to account for its sparsity. The resulting optimization problem is called the :ref:`lasso`. We use the class :class:`~sklearn.linear_model.Lasso`, that uses the coordinate descent algorithm. Importantly, this implementation is more computationally efficient on a sparse matrix, than the projection operator used here. The reconstruction with L1 penalization gives a result with zero error (all pixels are successfully labeled with 0 or 1), even if noise was added to the projections. In comparison, an L2 penalization (:class:`~sklearn.linear_model.Ridge`) produces a large number of labeling errors for the pixels. Important artifacts are observed on the reconstructed image, contrary to the L1 penalization. Note in particular the circular artifact separating the pixels in the corners, that have contributed to fewer projections than the central disk. .. GENERATED FROM PYTHON SOURCE LINES 38-146 .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_tomography_l1_reconstruction_001.png :alt: original image, L2 penalization, L1 penalization :srcset: /auto_examples/applications/images/sphx_glr_plot_tomography_l1_reconstruction_001.png :class: sphx-glr-single-img .. code-block:: Python # Author: Emmanuelle Gouillart # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from scipy import ndimage, sparse from sklearn.linear_model import Lasso, Ridge def _weights(x, dx=1, orig=0): x = np.ravel(x) floor_x = np.floor((x - orig) / dx).astype(np.int64) alpha = (x - orig - floor_x * dx) / dx return np.hstack((floor_x, floor_x + 1)), np.hstack((1 - alpha, alpha)) def _generate_center_coordinates(l_x): X, Y = np.mgrid[:l_x, :l_x].astype(np.float64) center = l_x / 2.0 X += 0.5 - center Y += 0.5 - center return X, Y def build_projection_operator(l_x, n_dir): """Compute the tomography design matrix. Parameters ---------- l_x : int linear size of image array n_dir : int number of angles at which projections are acquired. Returns ------- p : sparse matrix of shape (n_dir l_x, l_x**2) """ X, Y = _generate_center_coordinates(l_x) angles = np.linspace(0, np.pi, n_dir, endpoint=False) data_inds, weights, camera_inds = [], [], [] data_unravel_indices = np.arange(l_x**2) data_unravel_indices = np.hstack((data_unravel_indices, data_unravel_indices)) for i, angle in enumerate(angles): Xrot = np.cos(angle) * X - np.sin(angle) * Y inds, w = _weights(Xrot, dx=1, orig=X.min()) mask = np.logical_and(inds >= 0, inds < l_x) weights += list(w[mask]) camera_inds += list(inds[mask] + i * l_x) data_inds += list(data_unravel_indices[mask]) proj_operator = sparse.coo_matrix((weights, (camera_inds, data_inds))) return proj_operator def generate_synthetic_data(): """Synthetic binary data""" rs = np.random.RandomState(0) n_pts = 36 x, y = np.ogrid[0:l, 0:l] mask_outer = (x - l / 2.0) ** 2 + (y - l / 2.0) ** 2 < (l / 2.0) ** 2 mask = np.zeros((l, l)) points = l * rs.rand(2, n_pts) mask[(points[0]).astype(int), (points[1]).astype(int)] = 1 mask = ndimage.gaussian_filter(mask, sigma=l / n_pts) res = np.logical_and(mask > mask.mean(), mask_outer) return np.logical_xor(res, ndimage.binary_erosion(res)) # Generate synthetic images, and projections l = 128 proj_operator = build_projection_operator(l, l // 7) data = generate_synthetic_data() proj = proj_operator @ data.ravel()[:, np.newaxis] proj += 0.15 * np.random.randn(*proj.shape) # Reconstruction with L2 (Ridge) penalization rgr_ridge = Ridge(alpha=0.2) rgr_ridge.fit(proj_operator, proj.ravel()) rec_l2 = rgr_ridge.coef_.reshape(l, l) # Reconstruction with L1 (Lasso) penalization # the best value of alpha was determined using cross validation # with LassoCV rgr_lasso = Lasso(alpha=0.001) rgr_lasso.fit(proj_operator, proj.ravel()) rec_l1 = rgr_lasso.coef_.reshape(l, l) plt.figure(figsize=(8, 3.3)) plt.subplot(131) plt.imshow(data, cmap=plt.cm.gray, interpolation="nearest") plt.axis("off") plt.title("original image") plt.subplot(132) plt.imshow(rec_l2, cmap=plt.cm.gray, interpolation="nearest") plt.title("L2 penalization") plt.axis("off") plt.subplot(133) plt.imshow(rec_l1, cmap=plt.cm.gray, interpolation="nearest") plt.title("L1 penalization") plt.axis("off") plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 8.198 seconds) .. _sphx_glr_download_auto_examples_applications_plot_tomography_l1_reconstruction.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/applications/plot_tomography_l1_reconstruction.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/applications/plot_tomography_l1_reconstruction.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_tomography_l1_reconstruction.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_tomography_l1_reconstruction.py ` .. include:: plot_tomography_l1_reconstruction.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_