Mercurial > repos > PredictPDPS / changeset

--- a/README.md	Fri Apr 19 17:05:54 2024 +0300
+++ b/README.md	Fri Apr 19 16:34:59 2024 -0500
@@ -1,11 +1,11 @@
 
-# Julia codes for “Predictive online optimisation with applications to optical flow”
+# Julia codes for predictive online optimisation for dynamic inverse imaging problems
 
-This version of Julia codes contains the experiments in the manuscript _"Prediction techniques for dynamic imaging with online primal-dual methods"_ built on top of the experiments for the manuscript _“Predictive online optimisation with applications to optical flow”_ by [Tuomo Valkonen](https://tuomov.iki.fi) ([Journal of Mathematical Imaging and Vision](https://link.springer.com/article/10.1007/s10851-020-01000-4)).
+This version of Julia codes contains the experiments in the 2024 manuscript _"Prediction techniques for dynamic imaging with online primal-dual methods"_ by Neil Dizon, Jyrki Jauhiainen, and Tuomo Valkonen. It is built on top of, and includes the experiments for the 2019 article _“[Predictive online optimisation with applications to optical flow](https://arxiv.org/abs/2002.03053)”_ by [Tuomo Valkonen](https://tuomov.iki.fi).
 
 ## Prerequisites
 
-These codes were written for Julia 1.3. The Julia package prequisites are from November 2019 when our experiments were run, and have not been updated to maintain the same environment we used to do the experiments in the manuscript. You may get Julia from [julialang.org](https://julialang.org/). 
+These codes were written for Julia 1.9. The Julia package prequisites are from April 2024 when our experiments were run, and have not been updated to maintain the same environment we used to do the experiments in the manuscript. You may get Julia from [julialang.org](https://julialang.org/).
 
 ## Usage
 
@@ -21,23 +21,33 @@
 
     > using PredictPDPS
 
-This may take a while as Julia precompiles the code. Then, to generate all the experiments in the manuscript _“Predictive online optimisation with applications to optical flow”_, run:
+This may take a while as Julia precompiles the code.
+
+Below we document how to run the experiments for each article. See the source code for more details.
+
+To run the data generation multi-threadeadly parallel to the algorithm, set the `JULIA_NUM_THREADS` environment variable to a number larger than one.
+
+### Experiments for 2019 article
+
+To generate all the experiments for _“Predictive online optimisation with applications to optical flow”_, run:
 
     > batchrun_article()
 
-To see the experiments running visually, and not save the results, run 
+To see the experiments running visually, and not save the results, run
 
     > demo_known1()
 
-or any of `demo_XY()`, where `X`=`known`,`unknown` and `Y`=1,2,3.    
+or any of `demo_XY()`, where `X`=`known`,`unknown` and `Y`=1,2,3.
 
-Additionally, to generate all the experiments in the manuscript _"Prediction techniques for dynamic imaging with online primal-dual methods"_, run:
+### Experiments for 2014 article
+
+To generate all the experiments for _"Prediction techniques for dynamic imaging with online primal-dual methods"_, run:
 
     > batchrun_predictors()
 
 Both will save the results under `img/`.
 
-To see the experiments running visually, and not save the results, run 
+To see the experiments running visually, and not save the results, run
 
     > demo_denoising1()
 
@@ -47,6 +57,3 @@
 
 or any of `demo_denoisingZ()` for image stabilisation experiments and `demo_petZ()` for dynamic PET reconstruction where `Z=1` for Dual Scaling, `Z=2` for Greedy, `Z=3` for No Prediction, `Z=4` for Primal Only, `Z=5` for Proximal, and `Z=6` for Rotation predictors.
 
-See the source code for more details details.
-
-To run the data generation multi-threadeadly parallel to the algorithm, set the `JULIA_NUM_THREADS` environment variable to a number larger than one.