Compressive optical imaging with a photonic lantern

New single-pixel camera design, but this time using multicore fibers (MCF) and a photonic lantern instead of a spatial light modulator. Cool!

The fundamental idea is to excite one of the cores of a MCF. Then, light propagates through the fiber, which has a photonic lantern at the tip that generates a random-like light pattern at its tip. Exciting different cores of the MCF generates different light patterns at the end of the fiber, which can be used to obtain images using the single-pixel imaging formalism.

There is more cool stuff in the paper, for example the Compressive Sensing algorithm the authors are using, using positivity constraints. This is indeed quite relevant if you want to get high quality images, because of the reduced number of cores present in the MCF (remember, 1 core = 1 pattern, and the number of patterns determines the spatial resolution of the image in a single-pixel camera). It is also nice that there is available code from the authors here.

Some experimental/simulation results (nice Smash logo there!). Extracted from
Debaditya Choudhury et al., “Compressive optical imaging with a photonic lantern,” at https://arxiv.org/abs/1903.01288

Compressive optical imaging with a photonic lantern

by Debaditya Choudhury et al., at arXiv

Abstract:

The thin and flexible nature of optical fibres often makes them the ideal technology to view biological processes in-vivo, but current microendoscopic approaches are limited in spatial resolution. Here, we demonstrate a new route to high resolution microendoscopy using a multicore fibre (MCF) with an adiabatic multimode-to-singlemode photonic lantern transition formed at the distal end by tapering. We show that distinct multimode patterns of light can be projected from the output of the lantern by individually exciting the single-mode MCF cores, and that these patterns are highly stable to fibre movement. This capability is then exploited to demonstrate a form of single-pixel imaging, where a single pixel detector is used to detect the fraction of light transmitted through the object for each multimode pattern. A custom compressive imaging algorithm we call SARA-COIL is used to reconstruct the object using only the pre-measured multimode patterns themselves and the detector signals.

Weekly recap (29/04/2018)

This week we have a lot of interesting stuff:

Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms

Adaptive Optics + Light Sheet Microscopy to see living cells inside the body of a Zebra fish (the favorite fish of biologists!). Really impressive images overcoming scattering caused by tissue. You can read more about the paper on Nature and/or Howard Hughes Medical Institute.

 


The Feynmann Lectures on Physics online

I just read on OpenCulture that The Feynmann Lectures on Physics have been made available online. Until now, only the first part was published, but now you can also find volumes 2 and 3. Time to reread the classics…


Imaging Without Lenses

An interesting text appeared this week in American Scientist covering some aspects of the coming symbiosis between optics, computation and electronics. We are already able to overcome optical resolution, obtain phase information, or even imaging without using traditional optical elements, such as lenses. What’s coming next?


All-Optical Machine Learning Using Diffractive Deep Neural Networks

A very nice paper appeared on arXiv this week.

Xing Lin, Yair Rivenson, Nezih T. Yardimci, Muhammed Veli, Mona Jarrahi, Aydogan Ozcan

We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs that learned to implement handwritten digit classification and the function of an imaging lens at terahertz spectrum. With the existing plethora of 3D-printing and other lithographic fabrication methods as well as spatial-light-modulators, this all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs.

Imagine if Fourier Transforms were discovered before lenses, and then some day someone comes up with just a piece of glass and says “this can make the computations of FT at the speed of light”. Very cool read.


OPEN SPIN MICROSCOPY

I just stumbled upon this project while reading Lab on the Cheap. Seems like a very good resource if you plan to build a light-sheet microscope and do not wanna spend $$$$ on Thorlabs.


Artificial Inteligence kits from Google, updated edition

Last year, AIY Projects launched to give makers the power to build AI into their projects with two do-it-yourself kits. We’re seeing continued demand for the kits, especially from the STEM audience where parents and teachers alike have found the products to be great tools for the classroom. The changing nature of work in the future means students may have jobs that haven’t yet been imagined, and we know that computer science skills, like analytical thinking and creative problem solving, will be crucial.

We’re taking the first of many steps to help educators integrate AIY into STEM lesson plans and help prepare students for the challenges of the future by launching a new version of our AIY kits. The Voice Kit lets you build a voice controlled speaker, while the Vision Kit lets you build a camera that learns to recognize people and objects (check it out here). The new kits make getting started a little easier with clearer instructions, a new app and all the parts in one box.

To make setup easier, both kits have been redesigned to work with the new Raspberry Pi Zero WH, which comes included in the box, along with the USB connector cable and pre-provisioned SD card. Now users no longer need to download the software image and can get running faster. The updated AIY Vision Kit v1.1 also includes the Raspberry Pi Camera v2.

Looking forward to see the price tag and the date they become available.

The week in papers (22/04/18)

As a way to keep posts going, I am starting a short recap about interesting papers being published (or being discovered) every now and then. Probably I will write longer posts about some of them in the future.

Let’s get this thing going:

Two papers using ‘centroid estimation‘ to retrieve interesting information:

Extract voice information using high-speed camera

Mariko AkutsuYasuhiro Oikawa, and Yoshio Yamasaki, at The Journal of the Acoustical Society of America

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.

Realization of hybrid compressive imaging strategies

Recently I have been reading a lot about Compressive Sensing strategies. One of the things we always want when we work in a single-pixel architecture is to project the lowest possible number of masks, because the projecting process is the longest in all the acquisition procedure (and it gets longer and longer when you increase the spatial resolution of your images).

In the past, several strategies haven been implemented to reduce that number of projections. From going fully random to partially scan a basis at random and at the low frequency region, each approach presents some benefits and more or less speed gain.

In this work by the group of K.F. Kelly, they explored a different approach. Instead of chosing one measurement basis and design a sensing strategy (picking random elements, or centering around the low frequency part of the basis, or a mix), they create a measurement basis by merging different functions. They call it hybrid patterns. The basic idea is to chose a low number of patterns which work well for recovering low frequency content of natural images, and also some other patterns which are good to recover high frequency content. The novel thing here is that they do not require the patterns to belong to the same orthogonal basis, thus being able to carefully design its measurement basis. This provides very good quality results with a low number of projections.

Another thing I liked a lot was the Principal Component Analysis (PCA) part of the paper. Basically, they gathered a collection of natural images and they generated an orthogonal basis by using PCA. This leads me to think of PCA as a way of obtaining orthogonal bases where objects have their sparsest representation (maybe I am wrong about that).

Realization of hybrid compressive imaging strategies,

Y.Li et al, at Journal of the Optical Society of America A

(featured image exctracted from Fig.2 of the manuscript)

Abstract:

The tendency of natural scenes to cluster around low frequencies is not only useful in image compression, it also can prove advantageous in novel infrared and hyperspectral image acquisition. In this paper, we exploit this signal model with two approaches to enhance the quality of compressive imaging as implemented in a single-pixel compressive camera and compare these results against purely random acquisition. We combine projection patterns that can efficiently extract the model-based information with subsequent random projections to form the hybrid pattern sets. With the first approach, we generate low-frequency patterns via a direct transform. As an alternative, we also used principal component analysis of an image library to identify the low-frequency components. We present the first (to the best of our knowledge) experimental validation of this hybrid signal model on real data. For both methods, we acquire comparable quality of reconstructions while acquiring only half the number of measurements needed by traditional random sequences. The optimal combination of hybrid patterns and the effects of noise on image reconstruction are also discussed.

Fig3_Kelly
Really nice to see that PCA gives something very similar to DCT functions. This means that compressing images with DCT is really a good choice.

Experimental comparison of single-pixel imaging algorithms

I just read on ArXiv.org that L. Bian and his colleagues made a cool comparison between several ways of performing single-pixel imaging. They have tested the performance on several recovery procedures, some quite familiar but others not so well stablished. I find both Table 1 and Fig. 7 extremely interesting. One sums up really well the different reconstruction approaches that can be used in single-pixel imaging (with or without using Compressive Sensing). The figure points out one thing that experience has told me: every problem you try to solve usually needs an specific solver if you want to get good and fast results (which is extremely important when you start to work with BIG objects, as I plan to write soon here).

Experimental comparison of single-pixel imaging algorithms,

L. Biam et al, last revised 24 Oct 2017, https://arxiv.org/abs/1707.03164

(featured image extracted from Fig.7 of the manuscript)

Abstract:

Single-pixel imaging (SPI) is a novel technique capturing 2D images using a photodiode, instead of conventional 2D array sensors. SPI owns high signal-to-noise ratio, wide spectrum range, low cost, and robustness to light scattering. Various algorithms have been proposed for SPI reconstruction, including the linear correlation methods, the alternating projection method (AP), and the compressive sensing based methods. However, there has been no comprehensive review discussing respective advantages, which is important for SPI’s further applications and development. In this paper, we reviewed and compared these algorithms in a unified reconstruction framework. Besides, we proposed two other SPI algorithms including a conjugate gradient descent based method (CGD) and a Poisson maximum likelihood based method. Both simulations and experiments validate the following conclusions: to obtain comparable reconstruction accuracy, the compressive sensing based total variation regularization method (TV) requires the least measurements and consumes the least running time for small-scale reconstruction; the CGD and AP methods run fastest in large-scale cases; the TV and AP methods are the most robust to measurement noise. In a word, there are trade-offs between capture efficiency, computational complexity and robustness to noise among different SPI algorithms. We have released our source code for non-commercial use.

 

table