Direct Motif Extraction from High Resolution Crystalline STEM Images

• URL: http://arxiv.org/abs/2303.07438v1
• Date: Mon, 13 Mar 2023 19:35:54 GMT
• Title: Direct Motif Extraction from High Resolution Crystalline STEM Images
• Authors: Amel Shamseldeen Ali Alhasan, Siyuan Zhang, Benjamin Berkels
• Abstract summary: An automatic, unsupervised motif extraction is still not widely available yet. A novel multi-stage projection algorithm is used to determine primitive cell minimization. The method was tested on various synthetic and experimental HAADF STEM images.
• Score: 2.2660999029854536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During the last decade, automatic data analysis methods concerning different aspects of crystal analysis have been developed, e.g., unsupervised primitive unit cell extraction and automated crystal distortion and defects detection. However, an automatic, unsupervised motif extraction method is still not widely available yet. Here, we propose and demonstrate a novel method for the automatic motif extraction in real space from crystalline images based on a variational approach involving the unit cell projection operator. Due to the non-convex nature of the resulting minimization problem, a multi-stage algorithm is used. First, we determine the primitive unit cell in form of two lattice vectors. Second, a motif image is estimated using the unit cell information. Finally, the motif is determined in terms of atom positions inside the unit cell. The method was tested on various synthetic and experimental HAADF STEM images. The results are a representation of the motif in form of an image, atomic positions, primitive unit cell vectors, and a denoised and a modeled reconstruction of the input image. The method was applied to extract the primitive cells of complex $\mu$-phase structures Nb$_\text{6.4}$Co$_\text{6.6}$ and Nb$_\text{7}$Co$_\text{6}$, where subtle differences between their interplanar spacings were determined.

Related papers

• Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging [1.8687965482996822]
  Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution. We propose a segmentation-free deep learning approach that leverages grouped convolutions to learn interpretable embedded features from each imaging channel.
  arXiv  Detail & Related papers  (2024-11-02T11:21:33Z)
• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation [50.16004183320537]
  We describe a method for recovering the irradiance underlying a collection of images corrupted by atmospheric turbulence. We select one of the images as a reference, and model the deformation in this image by the aggregation of the optical flow from it to other images. We achieve state-of-the-art performance despite its simplicity.
  arXiv  Detail & Related papers  (2024-05-06T17:39:53Z)
• Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain [0.0]
  We propose a denoising diffusion probabilistic model (DDPM) trained on light-microscopic scans of cell-body stained sections. We show that our trained DDPM is able to generate highly realistic image information for this purpose, generating plausible cell statistics and cytoarchitectonic patterns.
  arXiv  Detail & Related papers  (2023-11-28T14:34:04Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Dual-View Selective Instance Segmentation Network for Unstained Live Adherent Cells in Differential Interference Contrast Images [11.762090096790823]
  Adherent cells have low contrast structures, fading edges, and irregular morphology. We developed a novel deep-learning algorithm for segmenting unstained adherent cells in DIC images. Our algorithm achieves an AP_segm of 0.555, which remarkably overtakes a benchmark by a margin of 23.6%.
  arXiv  Detail & Related papers  (2023-01-27T02:22:33Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• From augmented microscopy to the topological transformer: a new approach in cell image analysis for Alzheimer's research [0.0]
  Cell image analysis is crucial in Alzheimer's research to detect the presence of A$beta$ protein inhibiting cell function. We first found Unet is most suitable in augmented microscopy by comparing performance in multi-class semantics segmentation. We develop the augmented microscopy method to capture nuclei in a brightfield image and the transformer using Unet model to convert an input image into a sequence of topological information.
  arXiv  Detail & Related papers  (2021-08-03T16:59:33Z)
• Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning [52.07698484363237]
  We propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning. Our method achieves superior performance over many state-of-the-art algorithms for both the single image dehazing and hazy image understanding tasks.
  arXiv  Detail & Related papers  (2021-04-05T13:04:44Z)
• A Differential Model of the Complex Cell [24.756003635916613]
  This paper proposes an alternative model of the complex cell, based on Gaussian derivatives. It is most important to account for the insensitivity of the complex response to small shifts of the image. The relevance of the new model to the cortical image representation is discussed.
  arXiv  Detail & Related papers  (2020-12-09T10:23:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.