pure Python implementation of BM3D

This is a companion software for the submission: "Higher-Order Total Directional Variation: Imaging Applications" by Simone Parisotto , Jan Lellmann, Simon Masnou, and Carola-Bibiane Schönlieb. SIAM J. Imaging Sci., 13(4), 2063–2104. (42 pages)

3D image denoising using a modified U-Net architecture that exploits a prior image. Models are trained using efficient tensorflow pipeline based on keras and tf.data.Dataset API

This is the companion software for the paper "Total Directional Variation for video denoising".

BM3D & False Color Implemention

Video Denoising using Low Rank Matrix completion

Rust-based bm3d algorithm for ring artifact removal in normalized sinogram with Python Bindings

Advanced image processing nodes for ComfyUI with DeepInv, BM3D, wavelet denoising, and more

Electron density maps denoising algorithms implementation

Academic Project - This project afforded us a valuable opportunity to delve into the practical aspects of the Signal Processing field, with a specific emphasis on Image Processing. It involved a comparison between BM3D and Convolutional Autoencoders.

A deep learning model that finds out dental malpractice from tooth x-ray images.

Physics-informed 3D reconstruction from low-dose 2D images. Novel optimization framework achieving 0.45Å accuracy. Applications in medical imaging. (Manuscript in preparation)

Implementation and evaluation of classical and deep learning-based image denoising methods. The project compares Non-Local Means (NLM), BM3D, U-Net, and Denoising Autoencoders under different noise models (Gaussian, Salt & Pepper), using PSNR as the primary performance metric.

Master's Thesis: Denoising Methods for Multi-Dimensional Photoemission Spectroscopy

bm3d_core from https://github.com/ornlneutronimaging/bm3dornl — This is used as an operation in my wondermagick fork