Broadband lens and graphene/silicon photodiode for wide spectral imaging.

We designed a broadband lens along with a graphene/silicon photodiode for wide spectral imaging ranging from ultraviolet to near-infrared wavelengths. By using five spherical glass lenses, the broadband lens, with the modulation transfer function of 0.38 at 100 lp/mm, corrects aberrations ranging from 340 to 1700 nm. Our design also includes a broadband graphene/silicon Schottky photodiode with the highest responsivity of 0.63 A/W ranging from ultraviolet to near-infrared. By using the proposed broadband lens and the broadband graphene/silicon photodiode, several single-pixel imaging designs in ultraviolet, visible, and near-infrared wavelengths are demonstrated. Experimental results show the advantages of integrating the lens with the photodiode and the potential to realize broadband imaging with a single set of lens and a detector.

Guided Colorization Using Mono-Color Image Pairs.

Compared to color images captured by conventional RGB cameras, monochrome (mono) images usually have higher signal-to-noise ratios (SNR) and richer textures due to the lack of color filter arrays in mono cameras. Therefore, using a mono-color stereo dual-camera system, we can integrate the lightness information of target monochrome images with the color information of guidance RGB images to accomplish image enhancement in a colorization manner. In this work, based on two assumptions, we introduce a novel probabilistic-concept guided colorization framework. First, adjacent contents with similar luminance are likely to have similar colors. By lightness matching, we can utilize colors of the matched pixels to estimate the target color value. Second, by matching multiple pixels from the guidance image, if more of these matched pixels have similar luminance values to the target one, we can estimate colors with more confidence. Based on the statistical distribution of multiple matching results, we retain the reliable color estimates as initial dense scribbles and then propagate them to the rest of the mono image. However, for a target pixel, the color information provided by its matching results is quite redundant. Hence, we introduce a patch sampling strategy to accelerate the colorization process. Based on the analysis of the posteriori probability distribution of the sampling results, we can use much fewer matches for color estimation and reliability assessment. To alleviate incorrect color propagation in the sparsely scribbled regions, we generate extra color seeds according to the existed scribbles to guide the propagation process. Experimental results show that, our algorithm can efficiently and effectively restore color images with higher SNR and richer details from the mono-color image pairs, and achieves good performance in solving the color bleeding problem.

COMIRE: A Consistence-Based Mislabeled Instances Removal Method.

Training neural network classifiers (NNCs) usually requires all instances to be correctly labeled, which is difficult and/or expensive to satisfy in some practical applications. When label noise is present, mislabeled data will severely mislead the training of NNCs, resulting in poor generalization performance. In this work, we address the label noise issue by removing mislabeled instances from the training data. A COnsistence-based Mislabeled Instances REmoval (COMIRE) method is proposed. The main idea is based on the observation that during the training of the NNC, the training loss and the model's prediction uncertainty of correctly labeled instances show similar trends, while those of mislabeled instances have quite different trends. Thus, the consistency between the two trends can be used to distinguish correctly labeled instances from mislabeled ones. On this basis, an iteration scheme is introduced to further increase the separability between the two types of data. Experimental results show that COMIRE can effectively identify the mislabeled instances. Moreover, the classification performance is significantly improved after removing the identified instances from the noisy training data.

Induction of lncRNA MALAT1 by hypoxia promotes bone formation by regulating the miR-22-3p/CEBPD axis.

Adaptation to hypoxia promotes fracture healing. However, the underlying molecular mechanism remains unknown. Increasing evidence has indicated that long non-coding RNAs (lncRNAs) play crucial roles in several diseases, including fracture healing. In the present study, lncRNA microarray analysis was performed to assess the expression levels of different lncRNAs in MC3T3-E1 cells cultured under hypoxic conditions. A total of 42 lncRNAs exhibited significant differences in their expression, including metastasis associated lung adenocarcinoma transcript 1 (MALAT1), maternally expressed 3, AK046686, AK033442, small nucleolar RNA host gene 2 and distal-less homeobox 1 splice variant 2. Furthermore, overexpression of MALAT1 promoted osteoblast differentiation, alkaline phosphatase (ALP) activity and matrix mineralization of MC3T3-E1 cells, whereas its knockdown diminished hypoxia-induced cell differentiation, ALP activity and matrix mineralization in these cells. Moreover, functional analysis indicated that MALAT1 regulated the mRNA and protein expression levels of CCAAT/enhancer binding protein δ by competitively binding to microRNA-22-3p. Adenoviral-mediated MALAT1 knockdown inhibited fracture healing in a mouse model. Taken together, the results indicated that MALAT1 may serve a role in hypoxia-mediated osteogenesis and bone formation.

Robustness improvement for cyber physical system based on an optimization model of interdependent constraints.

With the rapid development of information technology, traditional infrastructure networks have evolved into cyber physical systems (CPSs). However, this evolution has brought along with it cyber failures, in addition to physical failures, which can affect the safe and stable operation of the whole system. In light of this, in this paper, we propose an interdependence-constrained optimization model to improve the robustness of the cyber physical system. The proposed model includes not only the realistic physical law but also the interdependence between the physical network and the cyber network. However, this model is highly nonlinear and cannot be solved directly. Therefore, we transform the model into a bi-level mixed integer linear programming problem, which can be easily and effectively solved in polynomial time. We conduct the simulation based on standard Institute of Electrical and Electronics Engineers test cases and study the impact of the disaster level and coupling strength on the robustness of the whole system. The simulation results show that our proposed model can effectively improve the robustness of the cyber physical system. Moreover, we compare the performance of the power supply in different CPSs, which have different network structures of the cyber network. Our work can provide useful instructions for system operators to improve the robustness of CPSs after extreme events happen in them.

Equivalent Continuous Formulation of General Hashing Problem.

Hashing-based approximate nearest neighbors search has attracted broad research interest, due to its low computational cost and fast retrieval speed. The hashing technique maps the data points into binary codes and, meanwhile, preserves the similarity in the original space. Generally, we need to solve a discrete optimization problem to learn the binary codes and hash functions, which is NP-hard. In the literature, most hashing methods choose to solve a relaxed problem by discarding the discrete constraints. However, such a relaxation scheme will cause large quantization error, which makes the learned binary codes less effective. In this paper, we present an equivalent continuous formulation of the discrete hashing problem. Specifically, we show that the discrete hashing problem can be transformed into a continuous optimization problem without any relaxations, while the transformed continuous optimization problem has the same optimal solutions and the same optimal value as the original discrete hashing problem. After transformation, the continuous optimization methods can be applied. We devise the algorithms based on the idea of DC (difference of convex functions) programming to solve this problem. The proposed continuous hashing scheme can be easily applied to the existing hashing models, including both supervised and unsupervised hashing. We evaluate the proposed method on several benchmarks and the results show the superiority of the proposed method compared with the state-of-the-art hashing methods.

Wavefront reconstruction based on deep transfer learning for microscopy.

The application of machine learning in wavefront reconstruction has brought great benefits to real-time, non-invasive, deep tissue imaging in biomedical research. However, due to the diversity and heterogeneity of biological tissues, it is difficult to train the dataset with a unified model. In general, the utilization of some unified models will result in the specific sample falling outside the training set, leading to low accuracy of the machine learning model in some real applications. This paper proposes a sensorless wavefront reconstruction method based on transfer learning to overcome the domain shift introduced by the difference between the training set and the target test set. We build a weights-sharing two-stream convolutional neural network (CNN) framework for the prediction of Zernike coefficient, in which a large number of labeled randomly generated samples serve as the source-domain data and the unlabeled specific samples serve as the target-domain data at the same time. By training on massive labeled simulated data with domain adaptation to unlabeled target-domain data, the network shows better performance on the target tissue samples. Experimental results show that the accuracy of the proposed method is 18.5% higher than that of conventional CNN-based method and the peak intensities of the point spread function (PSF) are more than 20% higher with almost the same training time and processing time. The better compensation performance on target sample could have more advantages when handling complex aberrations, especially the aberrations caused by various histological characteristics, such as refractive index inhomogeneity and biological motion in biological tissues.

An asymmetric interdependent networks model for cyber-physical systems.

Cyber-physical systems (CPSs) are integrations of information technology and physical systems, which are more and more significant in society. As a typical example of CPSs, smart grids integrate many advanced devices and information technologies to form a safer and more efficient power system. However, interconnection with the cyber network makes the system more complex, so that the robustness assessment of CPSs becomes more difficult. This paper proposes a new CPS model from a complex network perspective. We try to consider the real dynamics of cyber and physical parts and the asymmetric interdependency between them. Simulation results show that coupling with the communication network makes better robustness of power system. But since the influences between the power and communication networks are asymmetric, the system parameters play an important role to determine the robustness of the whole system.

Lighting Deviation Correction for Integrating-Sphere Multispectral Imaging Systems.

In an integrating sphere multispectral imaging system, measurement inconsistency can arise when acquiring the spectral reflectances of samples. This is because the lighting condition can be changed by the measured samples, due to the multiple light reflections inside the integrating sphere. Besides, owing to non-uniform light transmission of the lens and narrow-band filters, the measured reflectance is spatially dependent. To deal with these problems, we propose a correction method that consists of two stages. The first stage employs a white board to correct non-uniformity and a small white patch to correct lighting deviation, both under the assumption of ideal Lambertian reflection. The second stage uses a polynomial regression model to further remove the lighting inconsistency when measuring non-Lambertian samples. The method is evaluated on image data acquired in a real multispectral imaging system. Experimental results illustrate that our method eliminates the measurement inconsistency considerably. This consequently improves the spectral and colorimetric accuracy in color measurement, which is crucial to practical applications.

Distributed Graph Hashing.

Recently, hashing-based approximate nearest neighbors search has attracted considerable attention, especially in big data applications, due to its low computation cost and fast retrieval speed. In the literature, most of the existing hashing algorithms are centralized. However, in many large-scale applications, the data are often stored or collected in a distributed manner. In this situation, the centralized hashing methods are not suitable for learning hash functions. In this paper, we consider the distributed learning to hash problem. We propose a novel distributed graph hashing model for learning efficient hash functions based on the data distributed across multiple agents over network. The graph hashing model involves a graph matrix, which contains the similarity information in the original space. We show that the graph matrix in the proposed distributed hashing model can be decomposed into multiple local graph matrices, and each local graph matrix can be constructed by a specific agent independently, with moderate communication and computation cost. Then, the whole objective function of the distributed hashing model can be represented by the sum of local objective functions of multiple agents, and the hashing problem can be formulated as a nonconvex constrained distributed optimization problem. For tractability, we transform the nonconvex constrained distributed optimization problem into an equivalent bi-convex distributed optimization problem. Then we propose two algorithms based on the idea of alternating direction method of multipliers to solve this problem in a distributed manner. We show that the proposed two algorithms have moderate communication and computational complexities, and both of them are scalable. Experiments on benchmark datasets are given to demonstrate the effectiveness of the proposed methods.

Multispectral Image Super-Resolution via RGB Image Fusion and Radiometric Calibration.

Multispectral imaging is of wide application for its capability in acquiring the spectral information of scenes. Due to hardware limitation, multispectral imaging device usually cannot achieve high-spatial resolution. To address the issue, this paper proposes a multispectral image super-resolution algorithm, referred as SRIF, by fusing the low-resolution multispectral image and the high-resolution (HR) RGB image. It deals with the general circumstance that image intensity is linear to scene radiance for multispectral imaging devices while is nonlinear and unknown for most RGB cameras. The SRIF algorithm first solves the inverse camera response function and a spectral sensitivity function of RGB camera, and establishes the linear relationship between multispectral and RGB images. Then the unknown HR multispectral image is efficiently reconstructed according to the linear image degradation models. Meanwhile, the edge structure of the reconstructed HR multispectral image is kept in accordance with that of the RGB image using a weighted total variation regularizer. The effectiveness of the SRIF algorithm is evaluated on both public datasets and our image set. Experimental results validate that the SRIF algorithm outperforms the state-of-the-arts in terms of both reconstruction accuracy and computational efficiency.

Machine learning guided rapid focusing with sensor-less aberration corrections.

Non-invasive, real-time imaging and deep focus into tissue are in high demand in biomedical research. However, the aberration that is introduced by the refractive index inhomogeneity of biological tissue hinders the way forward. A rapid focusing with sensor-less aberration corrections, based on machine learning, is demonstrated in this paper. The proposed method applies the Convolutional Neural Network (CNN), which can rapidly calculate the low-order aberrations from the point spread function images with Zernike modes after training. The results show that approximately 90 percent correction accuracy can be achieved. The average mean square error of each Zernike coefficient in 200 repetitions is 0.06. Furthermore, the aberration induced by 1-mm-thick phantom samples and 300-µm-thick mouse brain slices can be efficiently compensated through loading a compensation phase on an adaptive element placed at the back-pupil plane. The phase reconstruction requires less than 0.2 s. Therefore, this method offers great potential for in vivo real-time imaging in biological science.

Dual inhibition of HY023016 based on binding properties of platelet membrane receptor subunit glycoprotein Ibα and thrombin exosites.

Thrombin has long been suggested as a desirable antithrombotic target, but anti-thrombin therapy without anti-platelet thereby has never achieved the ideal effect. HY023016 is a novel compound, in our previous study, it exerted better anti-thrombotic than dabigatran etexilate. The present study aims to illustrate the excess anti-thrombotic molecular mechanisms of HY023016 through thrombin anion exosites and the platelet membrane receptor subunit glycoprotein Ibα (GPIbα). HY023016 strongly inhibited the conversion of fibrinogen to fibrous may via blocking thrombin exosite I. We also discovered that HY023016 remarkably inhibited exosite II by a loss of affinity for the γ'-peptide of fibrinogen and for heparin. Furthermore, a solid phase binding assay revealed that HY023016 inhibited ristocetin-induced washed platelets bind to von Willebrand factor (vWF). In GST pull-down assay, HY023016 decreased the binding of recombinant vWF-A1 to GPIbα N-terminal. Thus, HY023016 provides an innovative idea for designing multi-targeted anti-thrombotic drugs and laying a scientific foundation for reducing "total thrombosis risk" in a clinical drug treatment.

Bidirectional texture function image super-resolution using singular value decomposition.

The bidirectional texture function (BTF) is widely employed to achieve realistic digital reproduction of real-world material appearance. In practice, a BTF measurement device usually does not use high-resolution (HR) cameras in data collection, considering the high equipment cost and huge data space required. The limited image resolution consequently leads to the loss of texture details in BTF data. This paper proposes a fast BTF image super-resolution (SR) algorithm to deal with this issue. The algorithm uses singular value decomposition (SVD) to separate the collected low-resolution (LR) BTF data into intrinsic textures and eigen-apparent bidirectional reflectance distribution functions (eigen-ABRDFs) and then improves the resolution of the intrinsic textures via image SR. The HR BTFs can be finally obtained by fusing the reconstructed HR intrinsic textures with the LR eigen-ABRDFs. Experimental results show that the proposed algorithm outperforms the state-of-the-art single-image SR algorithms in terms of reconstruction accuracy. In addition, thanks to the employment of SVD, the proposed algorithm is computationally efficient and robust to noise corruption.

Fast Multispectral Imaging by Spatial Pixel-Binning and Spectral Unmixing.

Multispectral imaging system is of wide application in relevant fields for its capability in acquiring spectral information of scenes. Its limitation is that, due to the large number of spectral channels, the imaging process can be quite time-consuming when capturing high-resolution (HR) multispectral images. To resolve this limitation, this paper proposes a fast multispectral imaging framework based on the image sensor pixel-binning and spectral unmixing techniques. The framework comprises a fast imaging stage and a computational reconstruction stage. In the imaging stage, only a few spectral images are acquired in HR, while most spectral images are acquired in low resolution (LR). The LR images are captured by applying pixel binning on the image sensor, such that the exposure time can be greatly reduced. In the reconstruction stage, an optimal number of basis spectra are computed and the signal-dependent noise statistics are estimated. Then the unknown HR images are efficiently reconstructed by solving a closed-form cost function that models the spatial and spectral degradations. The effectiveness of the proposed framework is evaluated using real-scene multispectral images. Experimental results validate that, in general, the method outperforms the state of the arts in terms of reconstruction accuracy, with additional 20× or more improvement in computational efficiency.

Spectral bidirectional texture function reconstruction by fusing multiple-color and spectral images.

Spectral bidirectional texture function (BTF) is essential for accurate reproduction of material appearance due to its nature of conveying both spatial and spectral information. A practical issue is that the acquisition of raw spectral BTFs is time-consuming. To resolve the limitation, this paper proposes a novel framework for efficient spectral BTF acquisition and reconstruction. The framework acquires red-green-blue (RGB) BTF images and just one spectral image. The full spectral BTFs are reconstructed by fusing the RGB and spectral images based on nonnegative matrix factorization (NMF). Experimental results indicate that the accuracy of spectral reflectance reconstruction is higher than that of existing algorithms. With the reconstructed spectral BTFs, the material appearance can be reproduced with high fidelity under various illumination conditions.

Empirical model for matching spectrophotometric reflectance of yarn windings and multispectral imaging reflectance of single strands of yarns.

The state-of-the-art multispectral imaging system can directly acquire the reflectance of a single strand of yarn that is impossible for traditional spectrophotometers. Instead, the spectrophotometric reflectance of a yarn winding, which is constituted by yarns wound on a background card, is regarded as the yarn reflectance in textile. While multispectral imaging systems and spectrophotometers can be separately used to acquire the reflectance of a single strand of yarn and corresponding yarn winding, the quantitative relationship between them is not yet known. In this paper, the relationship is established based on models that describe the spectral response of a spectrophotometer to a yarn winding and that of a multispectral imaging system to a single strand of yarn. The reflectance matching function from a single strand of yarn to corresponding yarn winding is derived to be a second degree polynomial function, which coefficients are the solutions of a constrained nonlinear optimization problem. Experiments on 100 pairs of samples show that the proposed approach can reduce the color difference between yarn windings and single strands of yarns from 2.449 to 1.082 CIEDE2000 units. The coefficients of the optimal reflection matching function imply that the reflectance of a yarn winding measured by a spectrophotometer consists of not only the intrinsic reflectance of yarn but also the nonignorable interreflection component between yarns.

Photometric Stereo for General BRDFs via Reflection Sparsity Modeling.

This paper proposes a pixelwise photometric stereo method for object surfaces with general bidirectional reflectance distribution functions (BRDFs) via appropriate reflection modeling. The modeling is based on three general characteristics of reflection components, i.e., the smooth variation of diffuse reflection, the concentration of specular reflection, and the low-intensity nature of shadow. A graph, whose nodes are light directions, is introduced to model these characteristics. In the graph, the neighborhood of nodes is determined by finding the light sources with close directions. The smoothness of the diffuse component is termed as the summation of local variations under all light sources. The specular reflection is modeled by group sparsity, and the shadow is determined via weighted l1 -norm modeling. The optimization problem, which incorporates these three modeling terms, is cast as a second-order cone programming problem. The proposed method is evaluated on both synthetic and real-world scenes with both isotropic and anisotropic materials. The experimental results show that the method is effective for object surfaces with general BRDFs and outperforms the state-of-the-arts.

Multispectral Image Out-of-Focus Deblurring Using Interchannel Correlation.

Out-of-focus blur occurs frequently in multispectral imaging systems when the camera is well focused at a specific (reference) imaging channel. As the effective focal lengths of the lens are wavelength dependent, the blurriness levels of the images at individual channels are different. This paper proposes a multispectral image deblurring framework to restore out-of-focus spectral images based on the characteristic of interchannel correlation (ICC). The ICC is investigated based on the fact that a high-dimensional color spectrum can be linearly approximated using rather a few number of intrinsic spectra. In the method, the spectral images are classified into an out-of-focus set and a well-focused set via blurriness computation. For each out-of-focus image, a guiding image is derived from the well-focused spectral images and is used as the image prior in the deblurring framework. The out-of-focus blur is modeled as a Gaussian point spread function, which is further employed as the blur kernel prior. The regularization parameters in the image deblurring framework are determined using generalized cross validation, and thus the proposed method does not need any parameter tuning. The experimental results validate that the method performs well on multispectral image deblurring and outperforms the state of the arts.