EBSD-Superresolution: Adaptable Physics-Based Super-Resolution for Electron Backscatter Diffraction Maps

Devendra K. Jangid*, Neal R. Brodnik*, Michael G. Goebel, Amil Khan, SaiSidharth Majeti, McLean P. Echlin, Samantha H. Daly, Tresa M. Pollock, B.S. Manjunath

[* equal contirbution]

Paper || Poster

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Abstract: In computer vision, single image super-resolution (SISR) has been extensively explored using convolutional neural networks (CNNs) on optical images, but images outside this domain, such as those from scientific experiments, are not well investigated. Experimental data is often gathered using non-optical methods, which alters the metrics for image quality. One such example is electron backscatter diffraction (EBSD), a materials characterization technique that maps crystal arrangement in solid materials, which provides insight into processing, structure, and property relationships. We present a broadly adaptable approach for applying state-of-art SISR networks to generate super-resolved EBSD orientation maps. This approach includes quaternion-based orientation recognition, loss functions that consider rotational effects and crystallographic symmetry, and an inference pipeline to convert network output into established visualization formats for EBSD maps. The ability to generate physically accurate, high-resolution EBSD maps with super-resolution enables high throughput characterization and broadens the capture capabilities for three-dimensional experimental EBSD datasets.

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EBSD Framework

Installation

Step 1: Clone repo

  git clone "https://github.com/UCSB-VRL/EBSD-Superresolution.git"

Step 2: Create Virtual environment

  virtualenv -p /usr/bin/python3.6 ebsdr_sr_venv(name of virtual environment)

Step 3: Activate Virtual environment

  source ebsd_sr_venv/bin/activate

Step 4: Download Dependencies

  pip install -r requirements.txt

Step 5: Install gradual warmup scheduler. Go to pytorch-gradual-warmup-lr folder

   python setup.py install

Training

Run

./train.sh

Loss	dist_type	syms_req
L1	L1	False
L1 with symmetry	L1	True
Rotational distance approximation with symmetry	rot_dist_approx	True

Define the following parameters to train network

• --input_dir: "Directory Path to Datasets"
• --hr_data_dir: "Path to High Resolution EBSD Maps relative to input_dir"
• --val_lr_data_dir: "Path to Low Resolution EBSD Val Datasets"
• --val_hr_data_dir: "Path to High Resolution EBSD Val Datasets"
• --model: "Choose one of network architectures from edsr, rfdn, san, han"
• --save: "Folder name to save weights, loss curves and logs"

Important parameters in argparser.py

• --syms_req: "It tells whether you want to use symmetry or not during Loss calculation"
• --patch_size: "Size of Patch During Training"
• --act: "Activation Function in Network"
• --save_model_freq: "How frequently do you want to save models"

Evaluation

We will provide inference model on BisQue as module. You do not need to use following steps if you are using Bisque infrastructure.

Download trained weights for different networks trained with different losses from here

Put it in ./experiment/saved_weights/{name_of_file}/model/{name_of_file}.pt

For example

If you use edsr with L1 loss:

./experiment/saved_weights/edsr_l1_ti64/model/edsr_l1_ti64.pt

--model: edsr

--save: 'edsr_l1_ti64'

--model_to_load: 'edsr_l1_ti64'

--dist_type: 'l1'

--test_only
Run

./test.sh

The generated results will be saved at experiments/saved_weights/edsr_l1_ti64/results/Test_edsr_l1_ti64 in npy format. It will also generate images (.png) for each quaternion channel.

Visualization

The model will generate superresolved EBSD map in npy format. To convert into IPF maps from npy files, please see IPF Mapping

Results

Datasets

Material datasets will be available by request at discretion of authors.

Acknowledgements

This code is built on HAN, SAN, RCAN, and EDSR. We thank the authors for sharing their codes.

Citation

If you use EBSD-SR, please consider citing:

  @article{jangid2022adaptable,
  title={Adaptable physics-based super-resolution for electron backscatter diffraction maps},
  author={Jangid, Devendra K and Brodnik, Neal R and Goebel, Michael G and Khan, Amil and Majeti, SaiSidharth and Echlin, McLean P and Daly, Samantha H and Pollock, Tresa M and Manjunath, BS},
  journal={npj Computational Materials},
  volume={8},
  number={1},
  pages={255},
  year={2022},
  publisher={Nature Publishing Group UK London}
  }

Contact

Should you have any question, please contact [email protected] or [email protected]