Light transport and imaging through complex media & Photonics West 2018

Last ~20 days have been completely crazy. First, I went to a meeting organized by the Royal Society: Light transport and imaging through complex media. It was amazing. Beautiful place, incredible researchers, and a nice combination of signal processing and optical imaging. I am sure I will be looking for future editions.

After that, I assisted Photonics West. Both BIOS and OPTO were full of interesting talks. Scattering media, adaptive optics, DMDs, some compressive sensing… Fantastic week. There I talked about two recent works we made in Spain: balanced photodetection single-pixel imaging and phase imaging using a DMD and a lateral position detector. Both contributions were very well received, and I am happy with the feedback I got. So many new ideas… now I need some time to implement them! I plan on writing a bit here on the blog about the last work, which has been published in the last issue of Optica.

 

Some of the cool stuff I heard about:

Valentina Emiliani – Optical manipulation of neuronal circuits by optical wave front shaping. Very cool implementations combining multiple SLMs and temporal focusing to see how neurons work.

Richard Baraniuk – Phase retrieval: tradeoffs and a new algorithm. How to recover phase information from intensity measurements. Compressive sensing and inverse problems. Very interesting, and a really good speaker. It is difficult to find someone capable of explaining these concepts as easily as Richard.

Michael Unser – GlobalBioIm

When being confronted with a new imaging problem, the common experience is that one has to reimplement (if not reinvent) the wheel (=forward model + optimization algorithm), which is very time consuming and also acts as a deterrent for engaging in new developments. This Matlab library aims at simplifying this process by decomposing the workflow onto smaller modules, including many reusable ones since several aspects such as regularization and the injection of prior knowledge are rather generic. It also capitalizes on the strong commonalities between the various image formation models that can be exploited to obtain fast, streamlined implementations.

Oliver Pust – High spatial resolution hyperspectral camera based on a continously variable filter. Really cool concept of merging a continous filter and multiple expositions to obtain hyperspectral information and even 3D images.

Seungwoo Shin – Exploiting a digital micromirror device for a multimodal approach combinning optical diffraction tomography and 3D structured illumination microscopy. I am always happy to see cool implementations with DMDs. This is one of them. KAIST delivers.

We propose a multimodal system combining ODT and 3-D SIM to measure both 3-D RI and fluorescence distributions of samples with advantages including high spatiotemporal resolution as well as molecular specificity. By exploiting active illumination control of a digital micromirror device and two different illumination wavelengths, our setup allows to individually operate either ODT or 3-D SIM. To demonstrate the feasibility of our method, 3-D RI and fluorescence distributions of a planar cluster of fluorescent beads were reconstructed. To further demonstrate the applicability, a 3-D fluorescence and time-lapse 3-D RI distributions of fluorescent beads inside a HeLa cell were measured.

Post featured image extracted from here.

Optical companding

Christmas came and gone, and I am still trying to keep up with some papers I’ve read in the last months.

The guys at UCLA keep doing impressive stuff. First time I saw something from them was their work on Nature about ultrafast optical imaging (woah!).

This time they have proposed a way to improve the digitization of an electrical signal. Living in the time of the ‘great convergence’, every time we are more aware than Optics, Electronics, and Computer Science are closely related. Nowadays, in order to acquire optical information, one has almost always to deal with electrical signals in the analog domain, which need to be digitized before working with them in a computer. To do so, the most used tools are analog-to-digital converters (ADC). These instruments receive an electrical signal (analog), and convert it to a digital signal (a number representing the voltage or the current you are working with). This quantification sometimes results problematic, given that the full dynamic range of the signal (from the maximum to the minimum value) has to be divided in a finite number of steps (bins). If the signal presents very low variations, the bins might be not small enough to see the full details. One can try to see those details by amplifying the signal, but then the bigger values of the signal might be larger than the maximum value measurable by the ADC, provoking saturation.

Jalali’s group proposes to use Optical Companding to overcome this issue. The fundamental idea is to use optical processes that are not linear to compress the high amplitude signal parts, while amplifying the small amplitude signal values at the same time. After that, a traditional ADC digitizes the signal, and the knowledge about the optical compressor makes it possible to restore the original signal with great accuracy.

Optical Companding,

Yunshan Jiang, Bahram Jalali, submitted on 29 Dec 2017, https://arxiv.org/abs/1801.00007

(featured image exctracted from Fig. 1 of the manuscript)

Abstract,
We introduce a new nonlinear analog optical computing concept that compresses the signal’s dynamic range and realizes non-uniform quantization that reshapes and improves the signal-to-noise ratio in the digital domain.