Introduction

SDeconv is a library for 2D and 3D deconvolution of scientific images

Context

SDeconv has been developed in the Serpico research team. The goal is to provide a modular library to perform deconvolution of microscopy images. A classical application of our team is to apply deconvolution in 3D+t images depecting endosomes with Lattice LightSheet microscopy, and then ease the analysis.

Library components

SDeconv is written in python3 with pytorch. SDeconv library provides a module for each components of deconvolution algorithms:

• psfs: this module defines the interface to implement Point Spread Function generators.

• deconv: this module defines the interfaces to implement a deconvolution algorithm with or without neural networks

Furthermore, the library provides sample data, a command line interface, and a application programing interface to ease the integration of the sdeconv deconvolution algorithms into softwares.