In-line hologram segmentation for volumetric samples

We propose a fast, noniterative method to segment an in-line hologram of a volumetric sample into in-line subholograms according to its constituent objects. In contrast to the phase retrieval or twin image elimination algorithms, we do not aim or require to reconstruct the complex wave field of all the objects, which would be a more complex task, but only provide a good estimate about the contribution of the particular objects to the original hologram quickly. The introduced hologram segmentation algorithm exploits the special inner structure of the in-line holograms and applies only the estimated supports and reconstruction distances of the corresponding objects as parameters. The performance of the proposed method is demonstrated and analyzed experimentally both on synthetic and measured holograms. We discussed how the proposed algorithm can be efficiently applied for object reconstruction and phase retrieval tasks

Classification of Holograms with 3D-CNN

A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques of digital holography. We claim that a 2D convolutional network (CNN) cannot be efficient in decoding this volumetric information spread across the whole image as it inherently operates on local spatial features. Therefore, we propose a method, where we extract the volumetric information of the hologram by mapping it to a volume—using a standard wavefield propagation algorithm—and then feed it to a 3D-CNN-based architecture. We apply this method to a challenging real-life classification problem and compare its performance with an equivalent 2D-CNN counterpart. Furthermore, we inspect the robustness of the methods to slightly defocused inputs and find that the 3D method is inherently more robust in such cases. Additionally, we introduce a hologram-specific augmentation technique, called hologram defocus augmentation, that improves the performance of both methods for slightly defocused inputs. The proposed 3D-model outperforms the standard 2D method in classification accuracy both for in-focus and defocused input samples. Our results confirm and support our fundamental hypothesis that a 2D-CNN-based architecture is limited in the extraction of volumetric information globally encoded in the reconstructed hologram image

Programozható optoelektronikus tömbprocesszorok (POAC) és alkalmazásaik = Programmable opto-electric array processors (POAC) and their applications

Kidolgoztunk és megépítettünk egy hordozható programozható opto-elektronikus analogikus CNN számítógépet (Laptop-POAC), amelyet céltárgyak felismerésére és követésére alkalmaztunk. A POAC (programmabble optical analogic array computer) magprocesszora egy új típusu optikai korrelátor, amelyben bakteriorhodopsin (BR) filmet alkalmaztunk dinamikus (újraírható, többszörösen olvasható, törölhető) holografikus anyagként. Ez az optikai CNN implementáció egyesíti az optikai számítógépek nagy sebességét, rendkívűl nagy práhuzamosságát (milliós nagyságrendű csatornaszám) és a nagymértű templatek alkalmazhatóságát a CNN eszköszök rugalmas programozhatóságával. Különös jellemzője ennek az optikai tömbprocesszornak az, hogy a programozó templateket vagy egy kétdimenziós akuszto-optikai eltérítővel (ez esetben 64x64 pixel méretű templateket használtunk), vagy egy VCSEL lézer mátrixszal valósíthatjuk meg. A bemenő képeket 600x800 pixel felbontású folyadékkristályos mikromegjelenítővel vittük be. Meghatároztuk a jelenleg beszerezhető kulcselemekkel elérhető maximális felbontás és a sebesség korlátait. Ezáltal kimutatttuk, hogy a kispárhuzamosságú elektronikus adatátvitel (továbbá a létező CCD, CMOS és vizuális CNN chipek felbontás/sebesség) korlátai miatt 2D-s optika utófeldolgozásra van szükség és lehetőség. Új és hatékony céltárgy felismerő és több tárgy kvázi-egyidejű követésére alkalmas algoritmust dolgoztunk ki. Mérésekkel bizonyítottuk a berendezés és az algoritmusok hatékonyságát. | A portable programmable opto-electronic analogic CNN computer (Laptop-POAC) has been built and used to recognize and track targets. Its kernel processor is a novel type of high performance optical correlator based on the use of bacteriorhodopsin (BR) as a dynamic holographic material. This optical CNN implementation combines the optical computer's high speed, high parallelism (1 000 000 channel) and large applicable template sizes with flexible programmability of the CNN devices. A unique feature of this optical array computer is that programming templates can be applied either by a 2D acousto-optical deflector (up to 64x64 pixel size templates) incoherently or by VCSEL arrays. Input images are fed-in by a LCD-SLM of 600x800 pixel resolution. Evaluation of trade-off between speed and resolution is given. Novel and effective target recognition and multiple-target-tracking algorithms have been developed for the POAC. Tracking experiments are demonstrated. In the present model a video-speed CCD camera is recording the correlograms, however, later a CNN-UM chip and a high-speed CMOS camera will be applied for combined optical/electro-optical post-processing. An optical CNN template- and algorithm-library has been developed to solve a great variety of image processing tasks. It seems to be an important future work to expand this library

Special multicolor illumination and numerical tilt correction in volumetric digital holographic microscopy

We introduce a color imaging method in our digital holographic microscope system (DHM). This DHM can create color images of freely floating, or moving objects inside a large volume by simultaneously capturing three holograms using three different illumination wavelengths. In this DHM a new light source assembly is applied, where we use single mode fibers according to the corresponding wavelengths that are tightly and randomly arranged into a small array in a single FC/PC connector. This design has significant advantages over the earlier approaches, where all the used illuminations are coupled in the same fiber. It avoids the coupling losses and provides a cost effective, compact solution for multicolor coherent illumination. We explain how to determine and correct the different fiber end positions caused tilt aberration during the hologram reconstruction process. To demonstrate the performance of the device, color hologram reconstructions are presented that can achieve at least 1 μm lateral resolution

Receptive field atlas and related CNN models

In this paper we demonstrate the potential of the cellular nonlinear/neural network paradigm (CNN) that of the analogic cellular computer architecture (called CNN Universal Machine | CNN-UM) in modeling different parts and aspects of the nervous system. The structure of the living sensory systems and the CNN share a lot of features in common: local interconnections ("receptive field architecture"), nonlinear and delayed synapses for the processing tasks, the potentiality of feedback and using the advantages of both the analog and logic signal-processing mode. The results of more than ten years of cooperative work of many engineers and neurobiologists have been collected in an atlas: what we present here is a kind of selection from these studies emphasizing the exibility of the CNN computing: visual, tactile and auditory modalities are concerned