Progressive compressive sensing of large images with multiscale deep learning reconstruction

Progressive compressive sensing of large images with multiscale deep learning reconstruction

Blog Article

Abstract Compressive sensing (CS) is a sub-Nyquist sampling framework that has been employed to improve the performance of numerous imaging applications during the last 15 years.Yet, its application for large and high-resolution Backpack imaging remains challenging in terms of the computation and acquisition effort involved.Often, low-resolution imaging is sufficient for most of the considered tasks and only a fraction of cases demand high resolution, but the problem is that the user does not know in advance when high-resolution acquisition is required.

To address this, we propose a multiscale progressive CS method for the high-resolution imaging.The progressive sampling refines the resolution of the image, while incorporating the already sampled low-resolution information, making the process highly efficient.Moreover, the multiscale property of the progressively sensed samples is capitalized for a fast, deep learning (DL) reconstruction, otherwise infeasible due to practical limitations of training on high-resolution images.

The progressive CS and the multiscale reconstruction method are analyzed numerically and demonstrated experimentally with a single pixel camera imaging system.We demonstrate 4-megapixel size progressive compressive Jumper/Pants imaging with about half the overall number of samples, more than an order of magnitude faster reconstruction, and improved reconstruction quality compared to alternative conventional CS approaches.