DeconvLR

DeconvLR is a open source CUDA implementation of accelerated Richard-Lucy Deconvolution algorithm regularized with total variation loss. This library is developed to recovered blurred image due to the spreading of point source in optical system. As far as we know, there is no other fully functional open source GPU accelerated implementation. This project is aim to develope an open source, high efficient library to process high resolution images of high quality.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

You need the following packages to get started.

*nix

make
g++ <= 5
CMake >= 3.6
Boost >= 1.59
CUDA >= 8.0

Windows

TODO I haven't exactly tested this on Windows. DLL export symbols are needed in the public header.

Build

1. Please clone this repository

   git clone https://github.com/liuyenting/DeconvLR.git

   or download and extract the tarball from release page.

   tar zxvf DeconRL.tar.gz

2. Go to source directory and create a new build output directory.

   cd DeconvLR
   mkdir build

3. We use cmake to do the heavy lifting.

   cd build
   cmake ..

   if everything runs smoothly, we can proceed with

   make

Running the demo

TODO Explain how to run the demo.

Asides from the demo, this library is intended to use as

std::string origImgFile = "data/bigradient/sample.tif";
std::string psfFile = "data/bigradient/psf_n15_z5.tif";

// load psf
ImageStack psf(psfFile);

// init the deconvlr
DeconvLR deconvWorker;
deconvWorker.setResolution(1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f);

// open the image
const ImageStack<uint16_t> input(origImgFile);
ImageStack<uint16_t> output(input, 0);

// use the first image to init the resources
deconvWorker.setVolumeSize(input.nx(), input.ny(), input.nz());
deconvWorker.setPSF(psf);

// run the deconv
deconvWorker.process(output, input);

Benchmark

TODO move benchmakr images from gh-page (in docs folder) to here.

Authors

• Liu, Yen-Ting - Initial work - liuyenting
• Chiang, Tin-Ray - Initial work - CTinRay

License

This project is licensed under the Apache License - see the LICENSE file for details

References

• William Hadley Richardson (1972), "Bayesian-Based Iterative Method of Image Restoration*," J. Opt. Soc. Am. 62, 55-59.
• Lucy, L. B. (1974). "An iterative technique for the rectification of observed distributions". Astronomical Journal. 79 (6): 745–754.
• Biggs, D. S., & Andrews, M. (1997). Acceleration of iterative image restoration algorithms. Applied optics, 36(8), 1766-1775.
• Dey, N., Blanc-Féraud, L., Zimmer, C., Roux, P., Kam, Z., Olivo-Marin, J. C., & Zerubia, J. (2004). 3D microscopy deconvolution using Richardson-Lucy algorithm with total variation regularization (Doctoral dissertation, INRIA).