Improved Bayesian image denoising based on wavelets with applications to electron microscopy

Sorzano, Carlos Óscar S.; Ortiz, Estela; López, Ma Dolores; Rodrigo, Javier

doi:10.1016/j.patcog.2005.12.009

Cited by 23 publications

(15 citation statements)

References 13 publications

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“…In our case, a careful image preprocessing is important in order to reduce noise and highlight the projections of the particle with respect to their background. The wavelet denoising is crucial in this task (Sorzano et al, 2006), and the posterior histogram partitioning also helps to highlight the structure being picked.…”

Section: Discussionmentioning

confidence: 99%

“…We propose to high-pass filter the image, then to apply a Bayesian wavelet denoising algorithm (Sorzano et al, 2006), downsampling the image by a factor two by removing all high frequency wavelet components, outlier rejection clipping all extremely small and high values, and histogram partitioning into a fixed (user supplied) number of bins. Fig.…”

Section: Methodsmentioning

confidence: 99%

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Automatic particle selection from electron micrographs using machine learning techniques

Sorzano

Recarte

Alcorlo

et al. 2009

Journal of Structural Biology

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The 3D reconstruction of biological specimens using Electron Microscopy is currently capable of achieving subnanometer resolution. Unfortunately, this goal requires gathering tens of thousands of projection images that are frequently selected manually from micrographs. In this paper we introduce a new automatic particle selection that learns from the user which particles are of interest. The training phase is semi-supervised so that the user can correct the algorithm during picking and specifically identify incorrectly picked particles. By treating such errors specially, the algorithm attempts to minimize the number of false positives. We show that our algorithm is able to produce datasets with fewer wrongly selected particles than previously reported methods. Another advantage is that we avoid the need for an initial reference volume from which to generate picking projections by instead learning which particles to pick from the user. This package has been made publicly available in the open-source package Xmipp.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automatic particle selection from electron micrographs using machine learning techniques

Sorzano

Recarte

Alcorlo

et al. 2009

Journal of Structural Biology

Self Cite

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“…An alternative approach (Sorzano et al, 2006) that is formulated as an optimization problem takes advantage of the additive-noise image-formation model and the linearity of the Discrete Wavelet Transform (Mallat, 1999). Since the noise follows a Gaussian distribution with zero mean and variance N , p(n) = G(n, 0, N ), and the signal and noise are assumed to be independent, then p(y | x) = p(n) = G(y − x, 0, N ).…”

Section: Problem 1: Image Denoisingmentioning

confidence: 99%

Optimization problems in electron microscopy of single particles

et al. 2006

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Electron Microscopy is a valuable tool for the elucidation of the three-dimensional structure of macromolecular complexes. Knowledge about the macromolecular structure provides important information about its function and how it is carried out. This work addresses the issue of three-dimensional reconstruction of biological macromolecules from electron microscopy images. In particular, it focuses on a methodology known as "single-particles" and makes a thorough review of all those steps that can be expressed as an optimization problem. In spite of important advances in recent years, there are still unresolved challenges in the field that offer an excellent testbed for new and more powerful optimization techniques.

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“…As a result, the amount of measurements needed to faithfully recover a sparse signal is primarily determined by the measure of incoherence between the measurement domain and the predefined sparse domain . Common choices for the predefined sparse domain for 2D high angle annular dark field scanning TEM (HAADF‐STEM) images are the discrete cosine domain, the Haar wavelet domain, and the gradient domain …”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Undersampling Schemes for Electron Tomography of Nanoparticles

Vanrompay

Béché

Verbeeck

et al. 2019

Part & Part Syst Charact

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One of the emerging challenges in the field of 3D characterization of nanoparticles by electron tomography is to avoid degradation and deformation of the samples during the acquisition of a tilt series. In order to reduce the required electron dose, various undersampling approaches have been proposed. These methods include lowering the number of 2D projection images, reducing the probe current during the acquisition, and scanning a smaller number of pixels in the 2D images. A comparison is made between these approaches based on tilt series acquired for a gold nanoparticle.

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Improved Bayesian image denoising based on wavelets with applications to electron microscopy

Cited by 23 publications

References 13 publications

Automatic particle selection from electron micrographs using machine learning techniques

Automatic particle selection from electron micrographs using machine learning techniques

Optimization problems in electron microscopy of single particles

Experimental Evaluation of Undersampling Schemes for Electron Tomography of Nanoparticles

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