rank-one projection

ROP inception

Here is a new short preprint: “ROP inception: signal estimation with quadratic random sketching”, available here and on arXiv. This is the first work of Rémi Delogne, carried out in collaboration with Vincent Schellekens and me.

ROP inception: signal estimation with quadratic random sketching

Abstract: Rank-one projections (ROP) of matrices and quadratic random sketching of signals support several data processing and machine learning methods, as well as recent imaging applications, such as phase retrieval or optical processing units.

Interferometric Lensless Endoscopy: Rank-one Projections of Image Frequencies with Speckle Illuminations

Abstract: Lensless endoscopy (LE) with multicore fibers (MCF) enables fluorescent imaging of biological samples at cellular scale. In this talk, we will see that under a common far-field approximation, the corresponding imaging process is tantamount to collecting multiple rank-one projections (ROP) of an Hermitian “interferometric” matrix–a matrix encoding a subsampling of the Fourier transform of the sample image.

Interferometric Lensless Endoscopy: Rank-one Projections of Image Frequencies with Speckle Illuminations

Abstract: Lensless endoscopy (LE) with multicore fibers (MCF) enables fluorescent imaging of biological samples at cellular scale. In this talk, we will see that under a common far-field approximation, the corresponding imaging process is tantamount to collecting multiple rank-one projections (ROP) of an Hermitian “interferometric” matrix–a matrix encoding a subsampling of the Fourier transform of the sample image.