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Digital holography can provide quantitative phase images related to the morphology and content of biological samples. After the numerical image reconstruction, the phase values are limited between −π and π; thus, discontinuity may occur due to the modulo 2π operation. We propose a new deep learning model that can automatically reconstruct unwrapped focused-phase images by combining digital holography and a Pix2Pix generative adversarial network (GAN) for image-to-image translation. Compared with numerical phase unwrapping methods, the proposed GAN model overcomes the difficulty of accurate phase unwrapping due to abrupt phase changes and can perform phase unwrapping at a twice faster rate. We show that the proposed model can generalize well to different types of cell images and has high performance compared to recent U-net models. The proposed method can be useful in observing the morphology and movement of biological cells in real-time applications.

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) beating can be efficiently characterized by time-lapse quantitative phase imaging (QPIs) obtained by digital holographic microscopy. Particularly, the CM’s nucleus section can precisely reflect the associated rhythmic beating pattern of the CM suitable for subsequent beating pattern characterization. In this paper, we describe an automated method to characterize single CMs by nucleus extraction from QPIs and subsequent beating pattern reconstruction and quantification. However, accurate CM’s nucleus extraction from the QPIs is a challenging task due to the variations in shape, size, orientation, and lack of special geometry. To this end, we propose a novel fully convolutional neural network (FCN)-based network architecture for accurate CM’s nucleus extraction using pixel classification technique and subsequent beating pattern characterization. Our experimental results show that the beating profile of multiple extracted single CMs is less noisy and more informative compared to the whole image slide. Applying this method allows CM characterization at the single-cell level. Consequently, several single CMs are extracted from the whole slide QPIs and multiple parameters regarding their beating profile of each isolated CM are efficiently measured.

Encrypted images obtained through double random phase-encoding (DRPE) occupy considerable storage space. We propose efficient compression schemes to reduce the size of the encrypted data. In the proposed schemes, two state-of-art compression methods of JPEG and JP2K are applied to the quantized encrypted phase images obtained by combining the DRPE algorithm with the virtual photon counting imaging technique. We compute the nonlinear cross-correlation between the registered reference images and the compressed input images to verify the performance of the compression of double random phase-encoded images. We show quantitatively through experiments that considerable compression of the encrypted image data can be achieved while security and authentication factors are completely preserved.

Digital propagation of an off-axis hologram can provide the quantitative phase-contrast image if the exact distance between the sensor plane (such as CCD) and the reconstruction plane is correctly provided. In this paper, we present a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best reconstruction distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately predict the propagation distance from a filtered hologram. The result is compared with the conventional automatic focus-evaluation function. Additionally, our approach can be utilized at the single-cell level, which is useful for cell-to-cell depth measurement and cell adherent studies.

This paper investigates the rhythm strip and parameters of synchronization of human induced pluripotent stem cell (iPS) derived cardiomyocytes. The synchronization is evaluated from quantitative phase images of beating cardiomyocytes which are obtained using the time-lapse digital holographic imaging method. By quantitatively monitoring the dry mass redistribution, digital holography provides the physical contraction-relaxation signal caused by autonomous cardiac action potential. In order to analyze the synchronicity at the cell-to-cell level, we extracted single cardiac muscle cells, which contain the nuclei, from the phase images of cardiomyocytes containing multiple cells resulting from the fusion of k-means clustering and watershed segmentation algorithms. We demonstrate that mature cardiomyocyte cell synchronization can be automatically evaluated by time-lapse microscopic holographic imaging. Our proposed method can be applied for studies on cardiomyocyte disorders and drug safety testing systems.

We propose methods to quantitatively calculate the fluctuation rate of red blood cells with nanometric axial and millisecond temporal sensitivity at the single-cell level by using time-lapse holographic cell imaging. For this quantitative analysis, cell membrane fluctuations (CMFs) were measured for RBCs stored at different storage times. Measurements were taken over the whole membrane for both the ring and dimple sections separately. The measurements show that healthy RBCs that maintain their discocyte shape become stiffer with storage time. The correlation analysis demonstrates a significant negative correlation between CMFs and the sphericity coefficient, which characterizes the morphological type of erythrocyte. In addition, we show the correlation results between CMFs and other morphological properties such as projected surface area, surface area, mean corpuscular volume, and mean corpuscular hemoglobin.

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm.

In recent years, many studies have focused on authentication of two-dimensional (2D) images using double random phase encryption techniques. However, there has been little research on three-dimensional (3D) imaging systems, such as integral imaging, for 3D image authentication. We propose a 3D image authentication scheme based on a double random phase integral imaging method. All of the 2D elemental images captured through integral imaging are encrypted with a double random phase encoding algorithm and only partial phase information is reserved. All the amplitude and other miscellaneous phase information in the encrypted elemental images is discarded. Nevertheless, we demonstrate that 3D images from integral imaging can be authenticated at different depths using a nonlinear correlation method. The proposed 3D image authentication algorithm can provide enhanced information security because the decrypted 2D elemental images from the sparse phase cannot be easily observed by the naked eye. Additionally, using sparse phase images without any amplitude information can greatly reduce data storage costs and aid in image compression and data transmission.

In this paper, we evaluate lossless and lossy compression techniques to compress quantitative phase images of red blood cells (RBCs) obtained by an off-axis digital holographic microscopy (DHM). The RBC phase images are numerically reconstructed from their digital holograms and are stored in 16-bit unsigned integer format. In the case of lossless compression, predictive coding of JPEG lossless (JPEG-LS), JPEG2000, and JP3D are evaluated, and compression ratio (CR) and complexity (compression time) are compared against each other. It turns out that JP2k can outperform other methods by having the best CR. In the lossy case, JP2k and JP3D with different CRs are examined. Because some data is lost in a lossy way, the degradation level is measured by comparing different morphological and biochemical parameters of RBC before and after compression. Morphological parameters are volume, surface area, RBC diameter, sphericity index, and the biochemical cell parameter is mean corpuscular hemoglobin (MCH). Experimental results show that JP2k outperforms JP3D not only in terms of mean square error (MSE) when CR increases, but also in compression time in the lossy compression way. In addition, our compression results with both algorithms demonstrate that with high CR values the three-dimensional profile of RBC can be preserved and morphological and biochemical parameters can still be within the range of reported values.

We present a new scheme for optical encryption of a binary image. In our method, the original binary data page is first divided into two identical pages. In each data page, the “on” and “off” pixels are represented by two discrete phases that are 90° apart. The first page corresponds to the phase conjugation of the second page, and vice versa. In addition, the wavefront of the two data pages is changed simultaneously from planar to spherical, for better encryption. The wavefront modification is represented by an extra phase shift, which is a function of position on the wavefront. In this way the two separate pages are both encrypted, and therefore the pages cannot be distinguished in a CCD. If the first page is used as an encrypted data page, then the second page is used as the decryption key, and vice versa. The decryption can be done by simply combining the two encrypted data pages. It is shown in our experiment that encryption and decryption can be fully accomplished in the optical domain.

Recently, double random phase encoding (DRPE) has been integrated with the photon counting (PC) imaging technique for the purpose of secure image authentication. In this scheme, the same key should be securely distributed and shared between the sender and receiver, but this is one of the most vexing problems of symmetric cryptosystems. In this study, we propose an efficient asymmetric image authentication scheme by combining the PC-DRPE and RSA algorithms, which solves key management and distribution problems. The retrieved image from the proposed authentication method contains photon-limited encrypted data obtained by means of PC-DRPE. Therefore, the original image can be protected while the retrieved image can be efficiently verified using a statistical nonlinear correlation approach. Experimental results demonstrate the feasibility of our proposed asymmetric image authentication method.

Red blood cell (RBC) phase images that are numerically reconstructed by digital holographic microscopy (DHM) can describe the cell structure and dynamics information beneficial for a quantitative analysis of RBCs. However, RBCs investigated with time-lapse DHM undergo temporal displacements when their membranes are loosely attached to the substrate during sedimentation on a glass surface or due to the microscope drift. Therefore, we need to develop a tracking algorithm to localize the same RBC among RBC image sequences and dynamically monitor its biophysical cell parameters; this information is helpful for studies on RBC-related diseases and drug tests. Here, we propose a method, which is a combination of the mean-shift algorithm and Kalman filter, to track a single RBC and demonstrate that the optical path length of the single RBC can be continually extracted from the tracked RBC. The Kalman filter is utilized to predict the target RBC position in the next frame. Then, the mean-shift algorithm starts execution from the predicted location, and a robust kernel, which is adaptive to changes in the RBC scale, shape, and direction, is designed to improve the accuracy of the tracking. Finally, the tracked RBC is segmented and parameters such as the RBC location are extracted to update the Kalman filter and the kernel function for mean-shift tracking; the characteristics of the target RBC are dynamically observed. Experimental results show the feasibility of the proposed algorithm.

The reconstruction of multiple depth images with a ray back-propagation algorithm in three-dimensional (3D) computational integral imaging is computationally burdensome. Further, a reconstructed depth image consists of a focus and an off-focus area. Focus areas are 3D points on the surface of an object that are located at the reconstructed depth, while off-focus areas include 3D points in free-space that do not belong to any object surface in 3D space. Generally, without being removed, the presence of an off-focus area would adversely affect the high-level analysis of a 3D object, including its classification, recognition, and tracking. Here, we use a graphics processing unit (GPU) that supports parallel processing with multiple processors to simultaneously reconstruct multiple depth images using a lookup table containing the shifted values along the x and y directions for each elemental image in a given depth range. Moreover, each 3D point on a depth image can be measured by analyzing its statistical variance with its corresponding samples, which are captured by the two-dimensional (2D) elemental images. These statistical variances can be used to classify depth image pixels as either focus or off-focus points. At this stage, the measurement of focus and off-focus points in multiple depth images is also implemented in parallel on a GPU. Our proposed method is conducted based on the assumption that there is no occlusion of the 3D object during the capture stage of the integral imaging process. Experimental results have demonstrated that this method is capable of removing off-focus points in the reconstructed depth image. The results also showed that using a GPU to remove the off-focus points could greatly improve the overall computational speed compared with using a CPU.

In this work, we evaluate the avalanche effect and bit independence properties of the double random phase encoding (DRPE) algorithm in the Fourier and Fresnel domains. Experimental results show that DRPE has excellent bit independence characteristics in both the Fourier and Fresnel domains. However, DRPE achieves better avalanche effect results in the Fresnel domain than in the Fourier domain. DRPE gives especially poor avalanche effect results in the Fourier domain when only one bit is changed in the plaintext or in the encryption key. Despite this, DRPE shows satisfactory avalanche effect results in the Fresnel domain when any other number of bits changes in the plaintext or in the encryption key. To the best of our knowledge, this is the first report on the avalanche effect and bit independence behaviors of optical encryption approaches for bit units.

AbstractStereo vision is becoming more and more common in three-dimensional visualization, autonomous vehicle navigation, path finding, object recognition, and other computer vision applications. In this paper, we present an approach for extracting visual landmarks from images acquired by a stereoscopic camera. The scale invariant feature transform (SIFT) technique with self-organizing map is used to detect and recognize visual landmarks. Our methodology is based on winner calculation technique, and the main idea is to keep in the database only distinctive features or landmarks in order to minimize detection time. We will demonstrate that this methodology is more efficient than ordinary SIFT algorithm or speeded up robust features (SURF) matching technique. Improved feature group matching with computation time better than SIFT and the SURF has been observed in the experiments with a variety of stereo image pairs.

Automatic operations play an important role in societies by saving time and improving efficiency. In this paper, we apply the digital image processing method to the field of lumbering to automatically calculate tree diameters in order to reduce culler work and enable a third party to verify tree diameters. To calculate the cross-sectional diameter of a tree, the image was first segmented by the marker-controlled watershed transform algorithm based on the hue saturation intensity (HSI) color model. Then, the tree diameter was obtained by measuring the area of every isolated region in the segmented image. Finally, the true diameter was calculated by multiplying the diameter computed in the image and the scale, which was derived from the baseline and disparity of correspondence points from stereoscopic image pairs captured by rectified configuration cameras.

In this paper, we present lossless compression of elemental images in photon-counting integral imaging. In order to verify the performance of the compression method applied to low light level three-dimensional (3D) integral imaging, we compute the correlation coefficient and peak to mean square error (PSNR) as metrics for 3D scene reconstruction integrity. We show quantitatively via experiments that a considerable compression of the elemental images in photon-counting integral imaging may be achievable without significant loss in the performance in terms of correlation and PSNR metrics. To the best of our knowledge, this is the first report on applying lossless compression algorithms in photon-counting 3D computational integral imaging.

Recent developments in 3-D computational optical imaging have ushered in a new era for biological research. Techniques in 3-D holographic microscopy integrated with numerical processing are enabling researchers to obtain rich, quantitative information about the structure of cells and microorganisms in noninvasive, real-time conditions.

In this paper we overview a method which can remove speckle noises to exist in coherent imaging systems. Integral imaging (II) system under coherent illumination records the elemental image set with speckle noise patterns of a threedimensional (3D) object. The computational geometrical ray propagation and statistical point estimation algorithms are applied to the elemental image set in order to reconstruct the speckle reduced 3D integral imaging. As performance metrics, the SNR and speckle index are calculated. The results are used to compare the speckle reduced 3D image reconstructed by the presented method with the coherent image having speckle patterns. It is shown in experiments that the presented method can three dimensionally reduce the speckle noise in the 3D object reconstruction.

We present an optical imaging system and mathematical algorithms for three-dimensional sensing and identification of stem cells. Data acquisition of stem cells is based on holographic microscopy in the Fresnel domain by illuminating the cells with a laser. In this technique, the holograms of stem cells are optically recorded with an image sensor array interfaced with a computer and three-dimensional images of the stem cells are reconstructed from the Gabor-filtered digital holograms. The Gabor wavelet transformation for feature extraction of the digital hologram is performed to improve the process of identification. The inverse Fresnel transformation of the Gabor-filtered digital hologram is performed to reconstruct the multi-scale three-dimensional images of the stem cells at different depths along the longitudinal direction. For recognition and classification of stem cells, a statistical approach using an empirical cumulative density function is introduced. The experiments indicate that the proposed system can be potentially useful for recognizing and classifying stem cells. To the best of our knowledge, this is the first report on using three-dimensional holographic microscopy for automated identification of stem cells.

Optical systems that can quickly detect and identify harmful microbes could become critical tools for preventing the next pandemic. Single-exposure on-line holographic microscopy may provide a way to image and identify microorganisms three dimensionally and in real time.

AbstractPhotorefractive composites based on carbazole-substituted polysiloxane, doped with a series of NLO chromophores and photo-sensitizer were presented. The alkyl end group of chromophore was observed to influence electro-optic and photoconductivity of composite, in addition to device stability. And the gain coefficient of composite containing 4-diethylaminobenzylidene malonitrile as a chromophore was over 100 cm−1at 60 V/μm. And gain of composite was ca.2.0 at 90 V/μm, which indicates that the pump beam was almost transferred to the signal beam.

AbstractBarbituric acid derivatives are synthesized as the electro-optic chromophores for photorefractive application. When doped with new chromophores the photorefractive composites which consist of polysiloxane with pendant carbazole as photoconducting host show very large gain coefficient (Γ = 219cm−1at 100V/μm).

AbstractPhotorefractive polymer composite was prepared with carbazole-substituted poly(siloxane) as a host polymer, NLO chromophore and 2,4,7-trinitro-9-fluorenone as a sensitizer. This composite exhibited excellent photorefractive property. The gain coefficient determined by 2-beam coupling measurement was 250 cm−1at 80 V/μm. And the composite was very stable toward phase separation.

AbstractWe utilized the first metathesis reaction to synthesize a new type of photorefractive polymers that contain both a carbazole moiety as a hole transporter and NLO chromophores, attached to π-conjugated backbones. These polymers show two maximum values of photocurrent around 350 and 700 nm.The electro-optic coefficient (r33) of the poled polymer films measured at the wavelength of 1.3 μmwas in the value of 10.1 pm/V. The nonresonat values of the third-order NLO coefficient, χ(3), of the resulting polymers were found to be 2.9 × 10−32esu.at the incident wavelength of 1.907 μm.

We present a 3D imaging method to reduce speckle noise that exists in coherent imaging systems. This approach is based on integral imaging (II). The elemental images set having speckle-noise patterns of a 3D object is obtained by II technique under coherent illumination. The computational geometrical ray-propagation algorithm is applied to the elemental images in order to reconstruct the original 3D object. A uniform probability-density function is assumed for modeling the phase distribution of the speckle patterns. The statistical point estimator is used for 3D speckle removal. Speckle index is calculated to compare the computational reconstruction using the proposed method with that of conventional coherent image degraded by speckle patterns for 3D object reconstruction and by object recognition. Experimental results are presented. The speckle index, mean square error, and signal-to-noise ratio are used as performance metrics and are shown to have been significantly improved by the proposed method to reduce speckle noise in the 3D object reconstruction. 3D reconstruction experiments of objects with reduced speckle noise are presented. To the best of our knowledge, this is the first report on 3D speckle removal using II and statistical estimation algorithms.

We present a statistical approach to recognize three-dimensional (3D) objects with a small number of photons captured by using integral imaging (II). For 3D recognition of the events, the photon-limited elemental image set of a 3D object is obtained using the II technique. A computational geometrical ray propagation algorithm and the parametric maximum likelihood estimator are applied to the photon-limited elemental image set to reconstruct the irradiance of the original 3D scene voxels. The sampling distributions for the statistical parameters of the reconstructed image are determined. Finally, hypothesis testing for the equality of the statistical parameters between reference and input data sets is performed for statistical classification of populations on the basis of sampling distribution information. It is shown that large data sets of photon-limited 3D images can be converted into sampling distributions with their own statistical parameters, resulting in a substantial data dimensionality reduction for processing.

We present a new statistical approach to real-time sensing and recognition of microorganisms using digital holographic microscopy. We numerically produce many section images at different depths along a longitudinal direction from the single digital hologram of three-dimensional (3D) microorganisms in the Fresnel domain. For volumetric 3D recognition, the test pixel points are randomly selected from the section image; this procedure can be repeated with different specimens of the same microorganism. The multivariate joint density functions are calculated from the pixel values of each section image at the same random pixel points. The parameters of the statistical distributions are compared using maximum likelihood estimation and statistical inference algorithms. The performance of the proposed system is illustrated with preliminary experimental results.