Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Reconstruction of image can be defined as the general problem of estimating a two-dimensionalobject from a partial version of this object (a limited set of "projections"). ,  propose new approach for image reconstruction based onsimple quasicrystals and L1 minimisation.  discuss the exact reconstruction of an image supposed to have small spectra.  show that simple model sets may be used as sampling set for exact recovery. Moreover, by eliminating a finite number of points from the simple model sets  still have exact recovery. This last aspect is very important for practical applications, e.g. lossy compression.  run our approch onbenchmark images data sets and show that the quasicrystal sampling is more performantthan the random uniform in terms of timeexecution when thedimension of the input image increases.
['Basarab Matei', 'Younès Bennani']
I am writing a research paper on a idea Pruned Simple Model Sets for Fast Exact Recovery of Image
[{"from": "human", "value": "I am writing a research paper on a idea Pruned Simple Model Sets for Fast Exact Recovery of Image"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Reconstruction of image can be defined as the general problem of estimating a two-dimensionalobject from a partial version of this object (a limited set of \"projections\"). ,  propose new approach for image reconstruction based onsimple quasicrystals and L1 minimisation.  discuss the exact reconstruction of an image supposed to have small spectra.  show that simple model sets may be used as sampling set for exact recovery. Moreover, by eliminating a finite number of points from the simple model sets  still have exact recovery. This last aspect is very important for practical applications, e.g. lossy compression.  run our approch onbenchmark images data sets and show that the quasicrystal sampling is more performantthan the random uniform in terms of timeexecution when thedimension of the input image increases."}]