We consider the problem of reconstructing an image from compressive measurements using a multi-resolution grid. In this context, the reconstructed image is divided into multiple regions, each one with a different resolution. This problem arises in situations where the image to reconstruct contains a certain region of interest (RoI) that is more important than the rest. Through a theoretical analysis and simulation experiments we show that the multi-resolution reconstruction provides a higher quality of the RoI compared to the traditional single-resolution approach.
@article{arxiv.1602.05941,
title = {Multi-resolution Compressive Sensing Reconstruction},
author = {Adriana Gonzalez and Hong Jiang and Gang Huang and Laurent Jacques},
journal= {arXiv preprint arXiv:1602.05941},
year = {2016}
}