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This repository contains the updating version of Sparse deconvolution.

The Sparse deconvolution is an universal post-processing framework for fluorescence (or intensity-based) image restoration, including xy (2D), xy-t (2D along t axis), and xy-z (3D) images. It is based on the natural priori knowledge of forward fluorescence imaging model: sparsity and continuity along xy-t (z) axes.

It is a part of publication. For algorithmic details, please refer to:

Weisong Zhao et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy, Nature Biotechnology (2021).

The content in the following wiki can also be found in the publication and the user-manual.

You can also find some fancy results and comparisons on my website