Skip to content

Latest commit

 

History

History
106 lines (63 loc) · 4.24 KB

FAQ.md

File metadata and controls

106 lines (63 loc) · 4.24 KB
Raw

FAQ

General

  1. What is the bm3dornl library?

    • bm3dornl is a Python library for removing streak artifacts in normalized sinograms to reduce ring artifacts in the final reconstruction. It uses a multiscale BM3D algorithm accelerated with CuPy and Numba.

  2. What is the purpose of the bm3dornl library?

    • The library aims to provide open-source, high-performance ring artifact removal for neutron imaging using advanced denoising techniques.

  3. Who are the contributors to the bm3dornl project?

    • Developed by the ORNL neutron software engineering team and maintained by the neutron imaging team, including MARS@HFIR and VENUS@SNS.

Installation

  1. How do I install the bm3dornl library?

    • See user guide for installation instructions.

    • For developers, follow these steps:

      . Clone the repository and checkout a feature branch (use next for latest features). . Create a virtual environment, activate it, and install dependencies from environment.yml. . Perform a developer install using pip install -e ..

  2. What are the system requirements for bm3dornl?

    • Requires Python 3.10 or later and a CUDA-enabled GPU for CuPy acceleration.

  3. How can I set up the environment to use bm3dornl?

    • Use the environment.yml file: conda env create -f environment.yml.

  4. Can I use bm3dornl without a GPU?

    • At present, the library requires a CUDA-enabled GPU for accelerated performance. We are working on a version that will allow CPU-only operation when a GPU is not available.

Usage

  1. Can you provide a basic example of how to use bm3dornl for ring artifact removal?

    • See the user guide for a step-by-step example.
    • Basic usage
    import numpy as np
from bm3dornl.bm3d import bm3d_ring_artifact_removal
sinogram_denoised = bm3d_ring_artifact_removal(np.array(sinogram_input), mode="simple")

  2. How do I use bm3dornl with CuPy for accelerated performance?

    • Ensure you have a CUDA-enabled GPU and CuPy installed. The library will automatically use CuPy for acceleration.

  3. What are the main functions provided by bm3dornl?

    • The main function is bm3d_ring_artifact_removal, which takes a normalized sinogram and returns a denoised sinogram with reduced ring artifacts.
    • It also provides functions to perform
      • fast estimate with FFT-based notch filter
      • block-matching, noise variance weighted hard-thresholding, and aggregation for noise-free estimate pilot
      • block-matching, collaborative filtering, and aggregation for final denoising
      • global fourier refiltering derived from a Wiener filter

Code and Implementation

  1. How does the bm3dornl library utilize Numba for performance optimization?

    • Numba accelerates functions by compiling Python code to machine code at runtime.

  2. Can you explain the process of block matching in bm3dornl?

    • Involves finding similar patches in an image and grouping them for collaborative denoising, leveraging similar patches to enhance denoising effectiveness.

  3. How is CuPy used in bm3dornl for GPU acceleration?

    • CuPy is used to perform computationally intensive tasks on the GPU, such as collaborative filtering and hard thresholding, to speed up the denoising process.

Documentation and Support

  1. Where can I find the official documentation for bm3dornl?

    • Available in the repository’s README and additional docs.

  2. How can I contribute to the bm3dornl project?

    • Fork the repository, make your changes, and submit a pull request. Follow the contribution guidelines in the README.

  3. Are there any tutorials available for learning bm3dornl?

    • Check the repository for example notebooks or additional tutorial links.

  4. Where can I report bugs or request features?

    • You can report bugs or request features by opening an issue in the GitHub repository.

  5. Is there a community or forum for bm3dornl users?

    • Currently, the primary community interaction happens through the GitHub issues and pull requests. Consider checking the repository for any updates on community forums or mailing lists.