Reconstruction of 3D Microstructures from 2D Images via Transfer Learning

Bostanabad, Ramin

doi:10.1016/j.cad.2020.102906

Cited by 69 publications

(44 citation statements)

References 77 publications

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“…It permits the removal of small regions that are disjoint forms the larger objects without distorting the small features within the large objects. Neural networks have been proposed and a variety of works in binary reconstruction of grayscale images [45,46]. This technique is mainly used in X-ray images and MRI images and helps in the detection of cancer.…”

Section: Binary Reconstruction Of Gray Scale and Nonlinear Processingmentioning

confidence: 99%

Solving Image Processing Critical Problems Using Machine Learning

Sharma

Gupta

Jaiswal

2021

Studies in Big Data

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The history of vision can go back around 543 BC. From fossil studies, researchers found out that no. of animal species floats out and soon develops the first animal with eyes. As evolution in bigger species around fifty percent of neurons in our body are involved in the visual processing. Vision allows human to perceive, comprehend, and understand surrounding them while computer vision aims to duplicate the impact of human vision electronically in the form of images. The history of making artificial vison starts around 1545, Gemma Frisius, and Encylopedie in eighteenth century. The foundation period of the concept lies from 1943 to 1956, during this period research is cantered towards understanding the concept of machine learning intelligence, artificial neural system, and automata theory computation. The first work in the field of Artificial intelligence in computer vision and image processing was done in MIT called "Summer Vision Project". The rise of artificial intelligence starts and it is used widely in every field of technology. As the technology advances machine learning is used in almost every field. This book chapter provides you some details how the machine learning is used in image processing and to solve the current problem and challenge in computer vison like: Denoising, Image compression, Image color enhancement, segmentation, etc. Machine learning algorithm like Neural Network, Support Vector Machine, Genetic Algorithm, Convolutional Neural Network, etc. were discussed in the book chapter.

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Section: Binary Reconstruction Of Gray Scale and Nonlinear Processingmentioning

confidence: 99%

Solving Image Processing Critical Problems Using Machine Learning

Sharma

Gupta

Jaiswal

2021

Studies in Big Data

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“…A approach based on transfer learning has been introduced to accomplish the extrapolation‐based reconstruction for a wide range of microstructures. A pretrained VGG19 deep convolutional neural network has been used to obtain the microstructural features and construct the objective function 27 . The DNN‐guided reconstruction procedure has been used to generate the statistically equivalent samples.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of 3D greyscale image for reservoir rock from a single image based on pattern dictionary

Teng

et al. 2021

Journal of Microscopy

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Most methods that model 3D porous media from 2D images are based on binary images. In this paper, we propose a method for reconstructing 3D greyscale isotropic porous media images from a single image. Our proposed method incorporates a fast‐sampling procedure to control the continuity and variability between adjoining reconstruction layers, a new similarity calculation method to obtain the most similar patterns from a pattern dictionary, and a central area simulation procedure to solve the block effect problem. The reconstruction results from application of our proposed method to a real reservoir 3D model obtained via computed tomography (CT) and a comparison with the original CT structure demonstrate that our proposed method can reproduce properties such as autocorrelation function, linear function, shape distribution, average shape factor, average pore radius size, average throat radius size, average pore volume, permeability and grey histogram. Further, the comparison results indicate that the statistical characteristics of the reconstructions match the training image and the CT model perfectly.

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“…Li et al 56 proposed a framework based on VGG19 for microstructure reconstruction and structure-property predictions. The pretrained VGG19 network was also utilized to reconstruct the 3D microstructures from 2D microstructures by Bostanabad 57 .…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Chemistry and Processing History Prediction From Materials Microstructure: Application to Spinodal Decomposition

2021

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Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-eld method can predict the spatio-temporal evolution of the materials' microstructure, they are not e cient techniques for predicting processing and chemistry if a speci c morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe-Cr-Co alloy created by the phase-eld method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained E cientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe-Cr-Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth.

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Reconstruction of 3D Microstructures from 2D Images via Transfer Learning

Cited by 69 publications

References 77 publications

Solving Image Processing Critical Problems Using Machine Learning

Solving Image Processing Critical Problems Using Machine Learning

Reconstruction of 3D greyscale image for reservoir rock from a single image based on pattern dictionary

Deep Learning Approach for Chemistry and Processing History Prediction From Materials Microstructure: Application to Spinodal Decomposition

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