Hello dear Researchers,
I have 2D ISAR data 360 (azimuth pulses) * 201 (data points), now when executing Compressive sensing based sparse reconstruction, it generates error for size exceeds (15 GB array). I need guidance to reconstruct image from sparsest data, for the given dimension, could anyone tell how may I implement the easiest algorithm to reconstruct image from sparse data for learning point of view?
I tried 2D-DWD which is threshold technique to make data sparse, no doubt it reduces computation time but also imaging resolution too, but to reconstruct high resolution I need to apply sparsity driven algorithm.
Thank you. I look forward to receive suggestion or any implemented script to adapt.
Dear Muhammad, it's not my expertise area. But, I can recommend you to other lationoamerican researchers in image processing, Cesar Aceros, Leiner Barba and Lorena Vargas. I can find us in Research Gate.
Iliana MARIA Rumbo Thank you for your prompt reply.
I shall check papers from these Researchers. I have solved the problem with array and now resolving resolution enhancement of target detection.
I appreciate your feedback.
The needed memory is essentially used to pseudo inverse calculations
The dimensions N.M shouldn't exceed 50k each. Deal with reduced matrices dimensions "short time"
The needed memory is essentially used to pseudo inverse calculations
The dimensions N.M shouldn't exceed 50k each. Deal with reduced matrices dimensions "short time"