Created by sebastien.popoff on 25/08/2020

## Using prior information for speeding up the measurement of fiber transmission matrices

[S. Li et al., arxiv, 2007.15891, (2020)]

Due to disorder and dispersion, knowing the transmission matrix of a multimode fiber is usually required to reconstruct an input image for endoscopic applications. In the general case, its characterization for a fiber allowing $$N$$ guided modes requires at least $$N$$ complex measurements. However, we usually have additional information, the most common one being that the matrix is never totally random, and usually sparse, when expressed in the mode basis. In this study, the authors use such prior information to reduce drastically the number of measurements for the transmission matrix estimation using the framework of compressed sensing. They demonstrate the validity of such an approach for endoscopic imaging through multimode fibers.

Created by sebastien.popoff on 17/07/2020

## Noninvasive incoherent imaging through scattering media based on wavefront shaping

[T. Yeminy and O. Katz., arxiv, 2007.03956 (2020)]

Wavefront shaping unlocked many exciting applications related to imaging through scattering media. However, they usually require to have some feedback from the object to observe, typically a guide-star generated by physically labeling the sample or by using ultrasound (that reduced the resolution). Other computer-based approaches recently developed relied on the memory-effect, which drastically limits the field of view, or requires a coherent illumination. In the present paper, T. Yeminy and O. Katz present a very simple approach that allows the reconstruction of an object hidden behind a scattering medium under incoherent illumination. It uses wavefront shaping of the scattered light together with an optimization procedure based on some assumptions about the object.

Created by sebastien.popoff on 09/09/2013

## Imaging with nature: Using a scattering medium as a universal scrambler for imaging by compressed sensing

[A. Liutkus et al., Sci. Rep. 5, (2014)]

The idea of compressive sensing is to acquire an image with fewer measurements than dictated by the Shannon-Nyquist theorem. In other words, an image divided into "pixels" can usually be reconstructed using fewer measurements than the total number of pixels. To do so, one needs a way to mix the information, so that any measurement contains at least a bit of information on any input element. Previous implementations of compressive sensing consisted of artificially designing hardware and a sampling procedure to generate randomness. In the present paper, the authors show that one can use a random scattering medium as a universal image scrambler. The light reflected from an image propagates through a layer of white paint and the field is measured on different receptors on the other side of the sample. By previously measuring the transmission matrix, the authors show that sparse images can be successfully reconstructed using compressed sensing techniques taking advantage of the randomness generated by multiple scattering.