Multi-resolution Compressive Sensing Reconstruction
This work addresses image reconstruction in compressive sensing for applications where specific regions are more critical, though it appears incremental as it builds on existing compressive sensing techniques.
The paper tackles the problem of reconstructing images from compressive measurements by using a multi-resolution grid to prioritize a region of interest, showing through analysis and simulations that this approach provides higher quality in the RoI compared to traditional single-resolution methods.
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.