Target High-resolution Image Research Articles

Hybrid sparse-representation-based approach to image super-resolution reconstruction

This paper presents a hybrid sparse-representation-based approach to single-image super-resolution reconstruction. Our main contribution is threefold: (1) jointly utilize nonlocal similarity of intensity image and low-rank property of gradient image under the framework of sparse representation; (2) incorporate both the high-resolution (HR) and low-resolution dictionaries into the reconstruction process; and (3) incorporate both the unknown HR image and the sparse coefficients into a single objective function. By alternatively minimizing the objective function with respect to the unknown HR image and the sparse coefficients, we get an estimate of the target HR image. Extensive experiments validate that compared with many state-of-the-art algorithms the proposed method yields comparable results for noiseless images and achieves much better results for noisy images.

Abstract A42: Systematic modulation of the physical microenvironment for characterization of cancer cell lines and primary tissue

Abstract Introduction: The tumor microenvironment includes a range of physical, chemical, and cellular components such as pressure, hypoxia, acidosis, the extracellular matrix (ECM), stromal, and immune cells. These factors can influence the gene and protein expression of the various types of cells that make up the tumor as well as influence the selection of cells that can thrive in that environment; however, typical cancer cell culture rarely uses hypoxia and pressure nor utilizes substrates similar to the native ECM. We designed a system study the influence of hypoxia, pressure, and native ECM conditions on cancer cell lines and primary cells, with the goal of creating a more relevant culture environment for translational studies. Methods: Here, we comprehensively studied how hypoxia, pressure, and ECM composition and organization influences cell biology and gene expression, using transcriptome profiling across a range of physiologically-relevant culturing conditions to mimic various tumor microenvironments found within the body. To do this, we utilized Xcell's primary cell culture platform which includes a custom bioreactor that allows us to control both oxygen concentration (0.1%-20% O2) and hydrostatic pressure levels (26 to 260 mmHg / 0.5 psig to 5 psig). Additionally, we studied the influence of biomimetic substrates by controlling ECM composition and organization (aligned or unaligned collagen at concentrations from 1-2.5 mg/ml +/- fibronectin 0.1-10 microgram/ml) as both 2D ECM coatings and 3D hydrogels. Cell lines studied included models for brain cancer (U-87, A172), pancreatic cancer (PANC10.05), and prostate cancer (DU-145, PC-3, 22Rv1, LNCaP). We performed high-resolution immunofluorescence imaging and western blot protein expression analysis of key targets, including immunotherapy targets CTLA-4, PD-1, and PD-L1. Additionally, we applied our ability to tune culturing conditions to represent physiological conditions in the body for the successful culture of fresh primary tissues from normal volunteers as well as cancer patients for downstream analysis studies focused on biomarker discovery and as a clinical tool for disease monitoring and treatment decision-making. Conclusions: We identified both common and unique gene signatures across these different cells lines, with the hypoxic conditions activating HIF1 pathway activation having common across the cells studied whereas pressure results in more restricted signatures. Some cancer cell lines and PBMCs differentially express immunotherapeutic targets, at low oxygen and high pressure culturing conditions, resulting in reduced expression of key targets. Moreover, biomimetic ECM studies revealed modulation of ECM to represent a “tumor”-like ECM dramatically altered cell growth, morphology, and focal adhesion organization. In a 3D hydrogel context, a “tumor”-like ECM and a hypoxic environment dramatically enhanced tumor cell invasion. Consistently, analysis of mRNA-seq data revealed alterations in gene expression profiles of immunotherapeutic and drug-target pathways involving CTLA-4 and AR signaling as well as EMT targets. In contrast, we observed increased CD47 and CD44 expression at low oxygen and high pressure culturing conditions in cancer cell lines and PBMCs. Thus, supporting the notion that these are possible drug targets for tumors that are characterized by low oxygen levels and high interstitial fluid pressure. Citation Format: Luke Cassereau, Bruce Adams, Tianna Chow, Ajuni Sohota, Charles Ryan, James Lim. Systematic modulation of the physical microenvironment for characterization of cancer cell lines and primary tissue. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A42.

SRLSP: A Face Image Super-Resolution Algorithm Using Smooth Regression With Local Structure Prior

The performance of traditional face recognition systems is sharply reduced when encountered with a low-resolution (LR) probe face image. To obtain much more detailed facial features, some face super-resolution (SR) methods have been proposed in the past decade. The basic idea of a face image SR is to generate a high-resolution (HR) face image from an LR one with the help of a set of training examples. It aims at transcending the limitations of optical imaging systems. In this paper, we regard face image SR as an image interpolation problem for domain-specific images. A missing intensity interpolation method based on smooth regression with a local structure prior (LSP), named SRLSP for short, is presented. In order to interpolate the missing intensities in a target HR image, we assume that face image patches at the same position share similar local structures, and use smooth regression to learn the relationship between LR pixels and missing HR pixels of one position patch. Performance comparison with the state-of-the-art SR algorithms on two public face databases and some real-world images shows the effectiveness of the proposed method for a face image SR in general. In addition, we conduct a face recognition experiment on the extended Yale-B face database based on the super-resolved HR faces. Experimental results clearly validate the advantages of our proposed SR method over the state-of-the-art SR methods in face recognition application.

Photonics-based real-time and high-resolution ISAR imaging of non-cooperative target

Real-time and high-resolution imaging is demonstrated based on field trial detection of a non-cooperative target using a photonics-based inverse synthetic aperture radar (ISAR). By photonic generation and de-chirping of broadband linear frequency modulation signals, the radar can achieve a high range resolution thanks to the large instantaneous bandwidth (8 GHz at the K band), as well as real-time ISAR imaging using low-speed analog-to-digital conversion (25 MSa/s). A small-size unmanned aerial vehicle is employed as the non-cooperative target, and ISAR imaging is realized with a resolution far better than those achieved by the previously reported photonics-based ISARs. The capability for real-time ISAR imaging is also verified with an imaging frame rate of 25 fps. These results validate that the photonics-based radar is feasible in practical real-time and high-resolution ISAR imaging applications.

High-resolution ISAR imaging of maneuvering targets based on the sparse representation of multiple column-sparse vectors

For inverse synthetic aperture radar (ISAR) imaging of a maneuvering target, the time-variance of Doppler shifts will produce blurred images over a long coherent processing interval (CPI). Compressed sensing (CS) theory indicates that the precise recovery of an unknown sparse signal can be achieved from a very limited number of measurements. A novel sparse reconstruction of multiple column-sparse vectors using an algorithm with a minor reconstruction error and low computational complexity is proposed here based on multiple measurement vectors (MMV) and the smoothed ℓ0 norm (SL0) algorithm. For ISAR imaging of maneuvering targets, the Doppler shifts remain nearly constant for a short CPI. The proposed algorithm can produce high resolution ISAR images of maneuvering targets with an extremely limited number of pulses. Simulation results demonstrate the effectiveness and computational efficiency of the proposed method.

Multiple-input-multiple-output radar superresolution three-dimensional imaging based on multidimensional smoothed L0

By exploiting the sparsity of radar target image, it is hopeful to obtain a high-resolution target image in multiple-input-multiple-output (MIMO) radar via a sparse representation (SR) method. However, for the three-dimensional (3-D) imaging, the conventional SR method has to convert the 3-D problem into the one-dimensional (1-D) problem. Thus, it will inevitably impose a heavy burden on the storage and computation. A multidimensional smoothed L0 (MD-SL0) algorithm is proposed based on the conventional smoothed L0 algorithm. The proposed MD-SL0 can directly apply to the multidimensional SR problem without transforming to the 1-D case. As a result, a MIMO radar 3-D imaging method via MD-SL0 is achieved with high computation efficiency and low storage burden. Finally, the effectiveness of the method is validated by the results of comparative experiments.

Abstract 5122: Characterization of the effects of hypoxia and hydrostatic pressure on the expression of immunotherapeutic targets on cell lines used for translational research

Abstract INTRODUCTION The characterization of the tumor microenvironment includes mechanotransduction, hypoxia, acidosis, and tissue remodeling. These factors can influence the gene and protein expression of various cell types that make up the tumor as well as influence the selection of cells that can thrive in a given microenvironment; however, typical cancer cell culture rarely uses hypoxia and pressure nor utilizes substrates similar to the native extracellular matrix (ECM). We designed a system to study the influence of hypoxia, pressure, and native ECM conditions on cancer cell lines and primary cells, with the goal of creating culturing environments relevant for translational studies involving key immunotherapeutic targets. METHODS First, we studied how hypoxia with or without pressure influences cell biology and gene expression, using transcriptome profiling across a range of physiologically-relevant culturing conditions to mimic various tumor microenvironments found within the body. To do this, we utilized Xcell's primary cell culture platform that allows for the precise control of oxygen concentration (0.1%-20% O2) and hydrostatic pressure levels (26 to 260 mmHg / 0.5 psig to 5 psig). Additionally, we studied the influence of biomimetic substrates by ECM composition and organization (aligned or unaligned collagen at concentrations from 1-2.5 mg/ml +/- fibronectin 0.1-10 microgram/ml). Cell lines studied include models for brain (U-87, A172), pancreatic (PANC10.05), and prostate cancer (DU-145, PC-3, 22Rv1, LNCaP). We performed high-resolution immunofluorescence imaging and western blot protein expression analysis of key targets, including immunotherapeutic targets CTLA-4, PD-1, and PD-L1. Finally, we applied these culturing conditions to characterize primary tissues obtained from cancer patients for studies focused on biomarker discovery, patient monitoring and treatment decision-making. CONCLUSIONS We identified both common and unique gene expression signatures across different cells lines, with hypoxic conditions activating HIF1 signaling, whereas hydrostatic pressure resulted in restricted signatures of high clinical value. Cancer cell lines and PBMCs differentially express immunotherapeutic targets, at low oxygen and high pressure culturing conditions, resulting in reduced expression of key drug targets. Analysis of mRNA-seq data revealed alterations in gene expression profiles of immunotherapeutic and drug-target pathways involving CTLA-4 and AR signaling. In contrast, we observed increased CD47 and CD44 expression at low oxygen and high pressure culturing conditions in cancer cell lines and immune cells. Therefore, these results support the presence of physiologically-relevant drug targets in tumors and immune cells characterized by low oxygen and high interstitial fluid pressure. Citation Format: Bruce A. Adams, James Lim, Luke Cassereau, Tianna Chow, Susan Bernstein. Characterization of the effects of hypoxia and hydrostatic pressure on the expression of immunotherapeutic targets on cell lines used for translational research. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5122.

Three-dimensional interferometric inverse synthetic aperture radar imaging of maneuvering target based on the joint cross modified Wigner-Ville distribution

Inverse synthetic aperture radar (ISAR) can achieve high-resolution two-dimensional images of maneuvering targets. However, due to the indeterminate relative motion between radar and target, ISAR imaging does not provide the three-dimensional (3-D) position information of a target and suffers from great difficulty in target recognition. To tackle this issue, a 3-D interferometric ISAR (InISAR) imaging algorithm based on the joint cross modified Wigner-Ville distribution (MWVD) is presented to form 3-D images of maneuvering targets. First, we form two orthogonal interferometric baselines with three receiving antennas to establish an InISAR imaging system. Second, after the uniform range alignment and phase adjustment, the joint cross MWVD is used for all range cell of each antenna pair to generate the separation of the scatterer as well as preserve the phase that contains position information of the scatterer. At last, the 3-D images of the target can be directly reconstructed from the distribution. Simulation results demonstrate the validity of the proposal.

An Adaptive ISAR-Imaging-Considered Task Scheduling Algorithm for Multi-Function Phased Array Radars

In multi-function phased array radar (MFPAR) systems, time and energy resources are allocated for different tasks so that the radar can perform various missions simultaneously. An effective scheduling algorithm is crucial to optimizing the overall performance of the MFPAR. There are various existing adaptive resource scheduling methods for the allocation of radar resources, but none of them consider the mission of imaging, a very important task. In this paper, an adaptive ISAR-imaging-considered task scheduling algorithm is proposed. Based on the sparse-aperture ISAR Compressive Sensing (CS) cognitive imaging techniques, the required resources for target imaging can be calculated from the online cognition of the target characteristics. Then the imaging mission is considered in the resource scheduling optimization model to realize adaptive allocation of radar resources and high-resolution imaging of multi targets. With the proposed algorithm, different tasks, such as tracking, searching and imaging, etc. can be implemented simultaneously, thus the radar efficiency is significantly improved. The effectiveness of the proposed method will be demonstrated by simulation experiments.

High-Resolution Three-Dimensional Imaging of Space Targets in Micromotion

High-resolution three-dimensional (3-D) radar imaging of space targets in micromotion plays a significant role in space target recognition and space situation awareness; thus, it has attracted extensive attention in recent years. Because of the fast rotation, some scattering centers are occluded by others, i.e., the scattering centers cannot be continuously illuminated by radar in the imaging interval, and their radar echoes are discontinuous. In this paper, a nonparametric 3-D imaging method based on scattering center trajectory association is proposed. It deals with target occlusion using the Riemannian manifold optimization and obtains focused imaging of targets in complex micromotion. The effectiveness of the proposed method is validated using simulated data.

Adaptive CLEAN algorithm for millimetre wave synthetic aperture imaging radiometer in near field

High-resolution millimetre wave images of contiguous targets often suffer from the influence of sidelobe artefacts, partial correlation between targets and so on. Owing to the characteristics of near-field synthetic aperture imaging radiometers [such as the changing point spread function (PSF) and slender sideline], the existing CLEAN algorithms are unsuitable for near-field synthetic aperture imaging. This study is devoted to establishing a novel CLEAN algorithm (named adaptive CLEAN) to clean the reconstructed millimetre wave images accurately. First, the characteristics of near-field synthetic aperture imaging are studied. Then, the adaptive CLEAN algorithm is established based on these characteristics. In this study, the authors amend the amplitude of the targets and select the matching PSF for them according to their azimuths. Unlike other CLEAN algorithms, the parameters of this adaptive CLEAN algorithm are calculated by a formula, which is concluded from a lot of simulation experiments. Finally, the effectiveness of the proposed adaptive CLEAN algorithm is tested by several simulation experiments, and the superiority is also demonstrated by comparing it with the existing CLEAN algorithm. The results demonstrate that the proposed method is an efficient, feasible algorithm for cleaning the reconstructed images, irrespective of the point or contiguous targets.

Compressed sensing of terahertz radar azimuth-elevation imaging

There is increasing interest in high-resolution radar imaging of targets, and the recent development of terahertz (THz) imaging technique provides the depiction ability of targets in detail. The compressed sensing theory is introduced into terahertz radar azimuth-elevation imaging for facilitating the increasing sampling pressure and obtaining the improved imagery by exploiting the block sparse structure of a target’s reflectivity distribution. Compared with the conventional processing of even block partition in block sparsity, a block-coherence definition for uneven block partition is proposed as a sensing configuration quality parameter, and its relationship to the imagery reconstruction performance is verified. Further, a contrast metric for evaluating the improved image of uneven block partition is discussed without the knowledge of a true imaging result. The electromagnetic calculation data are used for the verification of imaging.

Multiframe Superresolution Reconstruction Based on Self-Learning Method

One category of the superresolution algorithms widely used in practical applications is dictionary-based superresolution algorithms, which constructs a single high-resolution (HR) and high-clarity image from multiple low-resolution (LR) images. Despite the fact that general dictionary-based superresolution algorithms obtain redundant dictionaries from numerous HR-LR images, HR image distortion is unavoidable. To solve this problem, this paper proposes a multiframe superresolution reconstruction based on self-learning methods. First, multiple images from the same scene are selected to be both input and training images, and larger-scale images, which are also involved in the training set, are constructed from the learning dictionary. Then, different larger-scale images are constructed via repetition of the first step and the initial HR sets whose scale closely approximates that of the target HR image are finally obtained. Lastly, initial HR images are fused into one target HR image under the NLM idea, while the IBP idea is adopted to meet the global constraint. The simulation results demonstrate that the proposed algorithm produces more accurate reconstructions than those produced by other general superresolution algorithms, while, in real scene experiments, the proposed algorithm can run well and create clearer HR images from input images captured by cameras.

High-resolution ISAR imaging of maneuvering targets based on sparse reconstruction

Traditional range-instantaneous Doppler (RID) imaging methods for maneuvering targets have low resolution and poor capability of noise suppression. In this paper, a novel ISAR imaging method of maneuvering targets based on sparse reconstruction is proposed, which can provide a high-resolution and focused map of the spatial distribution of scatterers on the target. The proposed method exploits the sparse spatial feature of target scene to characterize the sparsity of the echo signal in its time–frequency domain, constructs the instantaneous Fourier-transform-based dictionary that can reflect the time-varying Doppler frequency and then transforms RID imaging into the problem of sparse reconstruction. After discriminating noise range cells from the range cells containing signal components, noise range cells are used for noise level estimation and signal range cells are used for signal reconstruction. Furthermore, the iteration weighted strategy for ISAR image generation is applied to enhance the signal components and suppress noise. Both simulated and real data experiments are performed to evaluate the proposed method, and the experimental results are presented to demonstrate the superiority of the proposed method over traditional imaging methods.

Randomized Switched Antenna Array FMCW Radar for Automotive Applications

Frequency-modulated continuous-wave (FMCW) methods and switched antenna arrays (SAAs) are very attractive for application in automotive radar because of their low hardware complexity. In traditional SAA radar, the receiving antenna array elements are successively switched according to their natural spatial order [referred to as sequential SAAs (SSAAs)]. For radars using both SSAA and FMCW methods, the phase difference caused by the target azimuth and time delay will be difficult to distinguish, thus resulting in delay-space coupling, which may lead to degradation of the road target observation. In this paper, randomized SAA (RSAA) FMCW radar is proposed. The new radar scheme has high delay/space resolution for target location detection and character extraction, and can also solve the coupling problem. The signal model of RSAA FMCW radar is derived and compared with that of SSAA radar. The influence of the antenna-array switching mode on the radar performance is also analyzed. A conventional zero-padding algorithm and a new clustering FOCUSS algorithm are both introduced for the signal processing of the new radar scheme. The numerical results illustrate the performance of RSAA FMCW radar in high-resolution target imaging and delay-space decoupling.

High-Resolution 3D Imaging of Precession Cone-Shaped Targets

By taking full advantage of the scattering center distribution in the high frequency regime, this paper proposes a three-dimensional (3D) imaging and micromotion parameter estimation method for cone-shaped targets, which obtains the true 3D scattering center distribution without the need for image scaling and estimates the micromotion parameters simultaneously. Further, it is robust to deal with shadowing effect and low signal-to-noise ratio (SNR). Imaging results of simulated data generated using both the fixed point scattering model and electromagnetic (EM) computation have proved the validity of the proposed method.

A transductive graphical model for single image super-resolution

Abstract The image super-resolution technique plays a critical role in many applications, such as digital entertainments and medical diagnosis. Recently, the super-resolution method has been focused on the neighbor embedding techniques. However, these neighbor embedding based methods cannot produce sparse neighbor weights. Furthermore, these methods would not reach minor reconstructing errors only based on low-resolution patch information, which will result in high computational complexity and large construction errors. This paper presents a novel super-resolution method that incorporates iterative adaptation into neighbor selection and optimizes the model with high-resolution patches. In particular, the proposed model establishes a transductive probabilistic graphical model in light of both the low-resolution and high-resolution patches. The weights of the low-resolution neighbor patches can be treated as priori information of the construction weights for the target high-resolution image. The quality of the desired image is greatly improved in the proposed super-resolution method. Finally, the effectiveness of the proposed algorithm is demonstrated with a variety of experiment results.

Compressive multi-mode superresolution display.

Compressive displays are an emerging technology exploring the co-design of new optical device configurations and compressive computation. Previously, research has shown how to improve the dynamic range of displays and facilitate high-quality light field or glasses-free 3D image synthesis. In this paper, we introduce a new multi-mode compressive display architecture that supports switching between 3D and high dynamic range (HDR) modes as well as a new super-resolution mode. The proposed hardware consists of readily-available components and is driven by a novel splitting algorithm that computes the pixel states from a target high-resolution image. In effect, the display pixels present a compressed representation of the target image that is perceived as a single, high resolution image.

High-Resolution Radar Imaging of Space Targets Based on HRRP Series

When wide or ultrawideband, low pulse repetition frequency (PRF) radar is applied to the imaging of space targets; it is highly possible that motion through range cell and azimuth under-sampling occurs, which will result in image smearing. To figure out this problem, this paper proposes a novel, three-step imaging method using the high-resolution range profile (HRRP) series. In the first step, high-quality HRRP series are obtained based on the theory of sparse signal representation. Then, based on the Kalman predictor and the minimum Euclidean distance criterion, motion and amplitude feature-based scatterer trajectory association is carried out to form the scatterer trajectory matrix, from which the scatterer locations are conveniently solved in the last step. Compared to the traditional imaging techniques based on Doppler analysis, the proposed method is able to mitigate the influence of azimuth under-sampling, and may provide a new solution to high-resolution imaging of targets moving nonuniformly in low PRF scenarios. Finally, simulations have proved the effectiveness of the proposed method.

Radar Coincidence Imaging: an Instantaneous Imaging Technique With Stochastic Signals

Motivated by classical coincidence imaging which has been realized in optical systems, an instantaneous microwave-radar imaging technique is proposed to obtain focused high-resolution images of targets without motion limitation. Such a radar coincidence imaging method resolves target scatterers based on measuring the independent waveforms of their echoes, which is quite different from conventional radar imaging techniques where target images are derived depending on time-delay and Doppler analysis. Due to the peculiar features of coincidence imaging, there are two potential advantages of the proposed imaging method over the conventional ones: 1) shortening the imaging time to even a pulse width without resolution deterioration so as to improve the performance of processing noncooperative targets and 2) simplifying the receiver complexity, resulting in a lower cost and platform flexibility in application. The basic principle of radar coincidence imaging is to employ the time-space independent detecting signals, which are produced by a multitransmitter configuration, to make scatterers located at different positions reflect independent waveforms from each other, and then to derive the target image based on the prior knowledge of this detecting signal spatial distribution. By constructing the mathematic model, the necessary conditions of the transmitting waveforms are analyzed for achieving radar coincidence imaging. A parameterized image-reconstruction algorithm is introduced to obtain high resolution for microwave radar systems. The effectiveness of this proposed imaging method is demonstrated via a set of simulations. Furthermore, the impacts of modeling error, noise, and waveform independence on the imaging performance are discussed in the experiments.