High-contrast Targets Research Articles

Efficient laser-driven proton acceleration from a petawatt contrast-enhanced second harmonic mixed-glass laser system

Efficient laser-driven plasma acceleration of ion beams requires precision control of the target–plasma profile, which is crucial to optimize the laser energy transfer. Along the laser propagation direction, this can be achieved by tailoring the temporal structure of the laser pulse. We show for the first time that frequency-doubling of a short pulse (hundreds-femtosecond range) petawatt-class mixed-glass laser system, which results in temporal intensity contrast enhancement, enables surface and volumetric laser–energy coupling, and the acceleration of proton beams from few-nanometer-thick foil targets. Experimentally, maximum ion energies and laser-to-proton energy conversion efficiencies were found to be both maximized at optimum laser and target conditions manifested when the normalized target density nearly equalizes the normalized laser vector potential, which is in agreement with theory and simulations. These signatures are recognized as a unique indication of the interaction between ultra-intense laser pulses with high temporal intensity contrast and ultra-thin nanometer-scale targets. Transverse modulations of accelerated proton beams in the form of bubble- and ring-like structures measured in the thinnest targets provide additional evidence of volumetric laser-driven particle acceleration regimes and transitional features in ultra-thin foil targets specific to laser–plasma interactions characterized by a high temporal intensity contrast. These results open avenues in the generation of high contrast laser pulses from short-pulse-femtosecond petawatt mixed-glass laser systems and demonstrate the feasibility of this technique for applications requiring high laser intensity contrast with high efficiency.

Quantitative Microwave Imaging of High-contrast Targets with the Incidence of Orbital Angular Momentum Wave

Quantitative Microwave Imaging of High-contrast Targets with the Incidence of Orbital Angular Momentum Wave

CBFM: Contrast Balance Infrared and Visible Image Fusion Based on Contrast-Preserving Guided Filter

Infrared (IR) and visible image fusion is an important data fusion and image processing technique that can accurately and comprehensively integrate the thermal radiation and texture details of source images. However, existing methods neglect the high-contrast fusion problem, leading to suboptimal fusion performance when thermal radiation target information in IR images is replaced by high-contrast information in visible images. To address this limitation, we propose a contrast-balanced framework for IR and visible image fusion. Specifically, a novel contrast balance strategy is proposed to process visible images and reduce energy while allowing for detailed compensation of overexposed areas. Moreover, a contrast-preserving guided filter is proposed to decompose the image into energy-detail layers to reduce high contrast and filter information. To effectively extract the active information in the detail layer and the brightness information in the energy layer, we proposed a new weighted energy-of-Laplacian operator and a Gaussian distribution of the image entropy scheme to fuse the detail and energy layers, respectively. The fused result was obtained by adding the detail and energy layers. Extensive experimental results demonstrate that the proposed method can effectively reduce the high contrast and highlighted target information in an image while simultaneously preserving details. In addition, the proposed method exhibited superior performance compared to the state-of-the-art methods in both qualitative and quantitative assessments.

Solving Electromagnetic Inverse Scattering Problems in Inhomogeneous Media by Deep Convolutional Encoder–Decoder Structure

This communication proposes a novel deep learning (DL) approach to solve electromagnetic inverse scattering (EMIS) problems in inhomogeneous media. The conventional approaches for solving inhomogeneous EMIS problems generally have to consider inhomogeneous Green’s functions or conduct approximation operations to media, which inevitably introduces various challenges, including complex mathematical derivation, high computation cost, unavoidable nonlinearity, and even strong ill-posedness. To avoid these challenges, we propose a DL approach based on the complex-valued deep convolutional neural networks (DConvNets), which comprise the deep convolutional encoder–decoder (DCED) structure. Its training data are collected based on the simple synthetic dataset. While the scattered fields received in the measurement domain are utilized as the input for the encoder to extract feature fragments, the final output for the counterpart decoder is the contrasts (permittivities) of dielectric scatterers in the target domain. In this way, unlike the conventional methods, the unknown domain between the target domain and measurement domain never has to be considered to compute inhomogeneous Green’s functions. Consequently, the inhomogeneous EMIS problems could be solved with higher accuracy even for extremely high-contrast targets. Numerical examples illustrate the feasibility of the proposed DL approach. It acts as a new candidate for solving EMIS problems in inhomogeneous media.

EPIPOLAR LINE-BASED LATERAL VIBRATION MEASUREMENT BY USING TWO CAMERAS

Abstract. Vibration measurement techniques can be categorized into contact-type and non-contact-type techniques. These types of techniques can add mass-loading to a lightweight structure resulting in the negative performance of a structure, because sensors, high contrast speckles or targets should be mounted on a structure. Moreover, non-contact-type vibration measurement techniques have only been tested to detect vibrations using a single camera. As the vibrations occurring at the opposite sides of a rotating structure in a region of interest (ROI) can be different from each other. For 3-dimensional (3D) vibration measurement, the same position in videos acquired from two cameras should be used. Because the videos acquired by two cameras placed perpendicular to the structure can be used to detect the vibrations in the x-direction as well as y-direction. In this study, an epipolar line-based corresponding point selection on a rotating cylindrical structure was performed, to extract the same ROIs from videos recorded by two cameras. A fundamental matrix was constructed by using the targets attached on the structure and in the background. The coordinates of the mid-pixel of the ROI in a video acquired by one camera was used to determine the epipolar line for the same ROI in the video acquired by another camera. Then an edge-based vibration measurement technique was applied to measure the vibration in the extracted ROIs. The results were used to reconstruct a 3D vibration signal. The 3D vibration measurement results can be used to effectively recognize the deformations resulting in the negative performance of a structure.

Effect of target contrast and divided attention on the useful field of view

Previous research has shown that there is a cost of dividing attention between the central and peripheral visual fields in a complex environment. However, it is not clear how stimulus factors, such as the contrast of the scene, affect the cost. The current study reports the results of two studies that address this question. In Experiment 1, temporal thresholds of the Useful Field of View (UFOV) tests were measured as a function of contrast and retinal eccentricity. The results showed that central-focused attention thresholds increased (i.e., performance decreased) as contrast decreased. Peripheral and divided attention task performance decreased as eccentricity increased. Surprisingly, peripheral and divided attention task performance were the best for medium rather than high contrast targets. The unexpected poorer performance under the high contrast condition might possibly be explained by the crowding effect. To test this possible explanation, in Experiment 2 the peripheral stimuli were simplified to minimize the potential crowding effect on peripheral target detection. The results showed that the unexpected effect of contrast on the cost of dividing attention could be accounted for by the crowding effect. When combined, the results from the two experiments suggest that the cost of dividing attention between central and peripheral targets is more pronounced for objects at greater eccentricity under lower contrast conditions, consistent with a tunnel effect. The implications of this finding are discussed in the paper.

Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Superresolution

Infrared small target super-resolution (SR) aims to recover reliable and detailed high-resolution image with high-contrast targets from its low-resolution counterparts. Since the infrared small target lacks color and fine structure information, it is significant to exploit the supplementary information among sequence images to enhance the target. In this paper, we propose the first infrared small target SR method named local motion and contrast prior driven deep network (MoCoPnet) to integrate the domain knowledge of infrared small target into deep network, which can mitigate the intrinsic feature scarcity of infrared small targets. Specifically, motivated by the local motion prior in the spatio-temporal dimension, we propose a local spatio-temporal attention module to perform implicit frame alignment and incorporate the local spatio-temporal information to enhance the local features (especially for small targets). Motivated by the local contrast prior in the spatial dimension, we propose a central difference residual group to incorporate the central difference convolution into the feature extraction backbone, which can achieve center-oriented gradient-aware feature extraction to further improve the target contrast. Extensive experiments have demonstrated that our method can recover accurate spatial dependency and improve the target contrast. Comparative results show that MoCoPnet can outperform the state-of-the-art video SR and single image SR methods in terms of both SR performance and target enhancement. Based on the SR results, we further investigate the influence of SR on infrared small target detection and the experimental results demonstrate that MoCoPnet promotes the detection performance. The code is available at <uri>https://github.com/XinyiYing/MoCoPnet</uri>.

Alternative constraints for improved multiplane hologram generation.

In this work, we introduce a modified hologram plane constraint to improve the accuracy of the global Gerchberg-Saxton (GGS) algorithm used for multiplane phase-only hologram generation. This constraint consists of a modified phase factor that depends on the amplitude of the field in the hologram plane. We demonstrate that this constraint produces an increase in the mean correlation coefficient between the reconstructed planes from a multiplane hologram and the corresponding amplitude targets for each plane. Furthermore, this constraint can be applied together with a mixed constraint in the reconstruction planes, leading to a more uniform and controllable reproduction of a target intensity distribution. To confirm the validity of our proposal, we show numerical and experimental results for multiplane holograms with six discrete planes, using both high and low contrast targets. For the experimental results, we implement a holographic projection scheme based on a phase-only spatial light modulator.

Reflectance estimation for infrared and visible image fusion

The desirable result of infrared (IR) and visible (VIS) image fusion should have textural details from VIS images and salient targets from IR images. However, detail information in the dark regions of VIS image has low contrast and blurry edges, resulting in performance degradation in image fusion. To resolve the troubles of fuzzy details in dark regions of VIS image fusion, we have proposed a method of reflectance estimation for IR and VIS image fusion. In order to maintain and enhance details in these dark regions, dark region approximation (DRA) is pro-posed to optimize the Retinex model.With the improved Retinex model based on DRA, quasi-Newton method is adopted to estimate the reflectance of a VIS image. The final fusion out-come is obtained by fusing the DRA-based reflectance of VIS image with IR image. Our method could simultaneously retain the low visibility details in VIS images and the high con-trast targets in IR images. Experiment statistic shows that compared to some advanced ap-proaches, the proposed method has superiority on detail preservation and visual quality.

Assessment of task-based image quality for abdominal CT protocols linked with national diagnostic reference levels

ObjectivesTo assess task-based image quality for two abdominal protocols on various CT scanners. To establish a relationship between diagnostic reference levels (DRLs) and task-based image quality.MethodsA protocol for the detection of focal liver lesions was used to scan an anthropomorphic abdominal phantom containing 8- and 5-mm low-contrast (20 HU) spheres at five CTDIvol levels (4, 8, 12, 16, and 20 mGy) on 12 CTs. Another phantom with high-contrast calcium targets (200 HU) was scanned at 2, 4, 6, 10, and 15 mGy using a renal stones protocol on the same CTs. To assess the detectability, a channelized Hotelling observer was used for low-contrast targets and a non-prewhitening observer with an eye filter was used for high contrast targets. The area under the ROC curve and signal to noise ratio were used as figures of merit.ResultsFor the detection of 8-mm spheres, the image quality reached a high level (mean AUC over all CTs higher than 0.95) at 11 mGy. For the detection of 5-mm spheres, the AUC never reached a high level of image quality. Variability between CTs was found, especially at low dose levels. For the search of renal stones, the AUC was nearly maximal even for the lowest dose level.ConclusionsComparable task-based image quality cannot be reached at the same dose level on all CT scanners. This variability implies the need for scanner-specific dose optimization.Key Points• There is an image quality variability for subtle low-contrast lesion detection in the clinically used dose range.• Diagnostic reference levels were linked with task-based image quality metrics.• There is a need for specific dose optimization for each CT scanner and clinical protocol.

Visual Performance of Center-distance Multifocal Contact Lenses Fit Using a Myopia Control Paradigm.

The purpose of this study was to examine the visual performance of center-distance MFCLs in nonpresbyopic adults under different illumination and contrast conditions compared with a single-vision contact lens (SVCL). Twenty-five adult subjects were fit with three different lenses (CooperVision Biofinity D MFCL +2.50 add, Visioneering Technologies NaturalVue MFCL, CooperVision Biofinity sphere). Acuity and reading performance were evaluated. A statistically significant difference in high-contrast distance acuity was observed (Biofinity, -0.18 ± 0.06; Biofinity MFCL, -0.14 ± 0.08; NaturalVue MFCL, -0.15 ± 0.03; repeated-measures [RM] ANOVA, P = .02). Under mesopic, high-contrast conditions, MFCLs performed worse than SVCLs (Biofinity, -0.05 ± 0.091; Biofinity MFCL, +0.03 ± 0.09; NaturalVue MFCL, +0.05 ± 0.091; RM-ANOVA, P < .0001). Under low-contrast conditions, MFCLs performed one line worse in photopic lighting and two lines worse under mesopic conditions (RM-ANOVA, P < .0001). Glare reduced acuity by 0.5 logMAR for all lenses (RM-ANOVA, P < .001). A statistically significant difference in near acuity was observed (RM-ANOVA, P = .02), but all lenses achieved acuity better than -0.1 logMAR (Biofinity, -0.16 ± 0.06; Biofinity MFCL, -0.17 ± 0.04; NaturalVue MFCL, -0.13 ± 0.08). Reading performance in words per minute (wpm) was worse with MFCLs (Biofinity MFCL, 144 ± 22 wpm; NaturalVue MFCL, 150 ± 28 wpm) than with SVCLs (156 ± 23 wpm; RM-ANOVA, P = .02) regardless of letter size (RM-ANOVA, P = .13). No difference in acuity between the MFCLs was detected (RM-ANOVA: all, P > .05). Multifocal contact lenses perform similarly to SVCLs for high-contrast targets and display reduced low-contrast acuity and reading speed. Practitioners should recognize that high-contrast acuity alone does not describe MFCL visual performance.

The lateral intraparietal sulcus takes viewpoint changes into account during memory-guided attention in natural scenes

Previous studies demonstrated that long-term memory related to object-position in natural scenes guides visuo-spatial attention during subsequent search. Memory-guided attention has been associated with the activation of memory regions (the medial-temporal cortex) and with the fronto-parietal attention network. Notably, these circuits represent external locations with different frames of reference: egocentric (i.e., eyes/head-centered) in the dorsal attention network vs. allocentric (i.e., world/scene-centered) in the medial temporal cortex. Here we used behavioral measures and fMRI to assess the contribution of egocentric and allocentric spatial information during memory-guided attention. At encoding, participants were presented with real-world scenes and asked to search for and memorize the location of a high-contrast target superimposed in half of the scenes. At retrieval, participants viewed again the same scenes, now all including a low-contrast target. In scenes that included the target at encoding, the target was presented at the same scene-location. Critically, scenes were now shown either from the same or different viewpoint compared with encoding. This resulted in a memory-by-view design (target seen/unseen x same/different view), which allowed us teasing apart the role of allocentric vs. egocentric signals during memory-guided attention. Retrieval-related results showed greater search-accuracy for seen than unseen targets, both in the same and different views, indicating that memory contributes to visual search notwithstanding perspective changes. This view-change independent effect was associated with the activation of the left lateral intra-parietal sulcus. Our results demonstrate that this parietal region mediates memory-guided attention by taking into account allocentric/scene-centered information about the objects' position in the external world.

GAN-FM: Infrared and Visible Image Fusion Using GAN With Full-Scale Skip Connection and Dual Markovian Discriminators

A good result of infrared and visible image fusion should not only maintain significant contrast for distinguishing targets from the backgrounds, but also contain rich scene textures to cater for human visual perception. However, previous fusion methods usually do not fully utilize the information, and hence their fused results sacrifice either the salience of thermal targets or the sharpness of textures. To address this challenge, we propose a novel Generative Adversarial Network with Full-scale skip connection and dual Markovian discriminators (GAN-FM) to fully preserve effective information in infrared and visible images. First, a full-scale skip connected generator is designed to extract and fuse deep features of different scales, which can promote the direct transmission of shallow high-contrast features to the deep level, preserving the thermal radiation targets from the semantic level. As a result, the fused image can maintain significant contrast. Second, we propose two Markovian discriminators to establish adversarial games with the generator, so as to estimate probability distributions of infrared and visible modalities at the same time. Unlike conventional global discriminator, the Markovian discriminators try to distinguish each patch of input images, thus the attention of network is restricted to local regions and the fused results are forced to contain more details. In addition, we propose an effective joint gradient loss to ensure the harmonious coexistence of contrast and texture, which prevents the background texture pollution caused by the edge diffusion of the high-contrast target regions. Extensive qualitative and quantitative experiments demonstrate that our GAN-FM has advantages over the state-of-the-art methods in preserving significant contrast and rich textures. Moreover, we apply the fused image generated by our method to object detection and image segmentation, which can effectively improve the performance.

Abrupt darkening under high dynamic range (HDR) luminance invokes facilitation for high-contrast targets and grouping by luminance similarity.

When scanning across a scene, luminance can vary by up to 100,000-to-1 (high dynamic range, HDR), requiring multiple normalizing mechanisms spanning from the retina to the cortex to support visual acuity and recognition. Vision models based on standard dynamic range (SDR) luminance contrast ratios below 100-to-1 have limited ability to generalize to real-world scenes with HDR luminance. To characterize how orientation and luminance are linked in brain mechanisms for luminance normalization, we measured orientation discrimination of Gabor targets under HDR luminance dynamics. We report a novel phenomenon, that abrupt 10- to 100-fold darkening engages contextual facilitation, distorting the apparent orientation of a high-contrast central target. Surprisingly, facilitation was influenced by grouping by luminance similarity, as well as by the degree of luminance variability in the surround. These results challenge vision models based solely on activity normalization and raise new questions that will lead to models that perform better in real-world scenes.

Enhanced Deep Learning Approach Based on the Deep Convolutional Encoder–Decoder Architecture for Electromagnetic Inverse Scattering Problems

This letter proposes a novel deep learning (DL) approach to resolve the electromagnetic inverse scattering (EMIS) problems. The conventional approaches of resolving EMIS problems encounter assorted difficulties, such as high contrast, high computational cost, inevitable intrinsic nonlinearity, and strong ill-posedness. To surmount these difficulties, a novel DL approach is proposed based on a novel complex-valued deep fully convolutional neural network structure. The proposed complex-valued deep learning model for solving EMIS problems composes of an encoder network and its corresponding decoder network, followed by a final pixel-wise regression layer. The complex-valued encoder network extracts feature fragments from received scattered field data, while the role of the complex-valued decoder network is mapping the feature fragments to retrieve the final contrasts (permittivities) of dielectric scatterers. Hence, the proposed deep learning model functions as an “heterogeneous” transformation process, where measured scattering field data is converted into the corresponding contrasts of scatterers. As a consequence, the EMIS problem could be resolved accurately even for extremely high-contrast targets. Numerical benchmarks have illustrated the feasibility and accuracy of our proposed approach. The proposed approach opens a novel path for the deep learning-based real-time quantitative microwave imaging for high-contrast scatterers.

A 100,000-to-1 high dynamic range (HDR) luminance display for investigating visual perception under real-world luminance dynamics

BackgroundReal-world illumination challenges both autonomous sensing and displays, because scene luminance can vary by up to 109-to-1, whereas vision models have limited ability to generalize beyond 100-to-1 luminance contrast. Brain mechanisms automatically normalize the visual input based on feature context, but they remain poorly understood because of the limitations of commercially available displays. New methodHere, we describe procedures for setup, calibration, and precision check of an HDR display system, based on a JVC DLA-RS600U reference projector, with over 100,000-to-1 luminance dynamic range (636–0.006055 cd/m2), pseudo 11 bit grayscale precision, and 3 ms temporal precision in the MATLAB/Psychtoolbox software environment. The setup is synchronized with electroencephalography (EEG) and infrared eye-tracking measurements. ResultsWe show display metrics including light scatter versus average display luminance (ADL), spatial uniformity, and spatial uniformity at high spatial frequency. We also show a luminance normalization phenomenon, contextual facilitation of a high contrast target, whose discovery required HDR display. Comparison with existing methodsThis system provides 100-fold greater dynamic range than standard 1000-to-1 contrast displays and increases the number of gray levels from 256 or 1024 (8 or 10 bits) to 2048 (pseudo 11 bits), enabling the study of mesopic-to-photopic vision, at the expense of spatial non-uniformities. ConclusionsThis HDR research capability opens new questions of how visual perception is resilient to real-world luminance dynamics and will lead to improved visual modeling of dense urban and forest environments and of mixed indoor-outdoor environments such as cockpits and augmented reality. Our display metrics code can be found at https://github.com/USArmyResearchLab/ARL-Display-Metrics-and-Average-Display-Luminance.

Accurate Near-Field Millimeter-Wave Imaging of Concave Objects—A Case Study of Dihedral Structures Under Monostatic Array Configurations

With the fast progress of high-resolution microwave and millimeter-wave (MMW) near-field imaging systems, image artifacts caused by complex high-contrast target become a nonnegligible issue for accurate surface reconstruction and recognition. Among different types of artifacts, those caused by high-order scattering within concave structures are particularly difficult to interpret and to correct. This article focuses on analyzing these unconsidered effects in conventional imaging or beamforming algorithms and chooses a typical imaging scenario with dihedral structures under near-field monostatic cylindrical imaging geometry for discussion. The mechanism of multiple reflections and the formation of artifacts are firstly investigated, and the artifacts are classified into two categories based on the number of reflection times. Then shooting and bouncing rays (SBRs) method is utilized to establish an analytical forward model including the mechanism of multiple reflections. Furthermore, a new imaging algorithm based on this forward model is proposed to obtain more accurate reconstructions. By compensating phase shifts of different multiple reflection signals with the corresponding range equations, dihedral-type objects with different opening angles can be accurately recovered. Numerical results are presented to verify the applicability of the forward model and to validate the effectiveness of the proposed imaging algorithm. In the end, experimental results confirm the practicability of the proposed method.

Failures in top-down control in schizophrenia revealed by patterns of saccadic eye movements.

Successful execution of many behavioral goals relies on well-organized patterns of saccadic eye movements, and in complex tasks, these patterns can reveal the component processes underlying task performance. The present study examined the pattern of eye movements in a visual search task to provide evidence of attentional control impairments in people with schizophrenia (PSZ). We tested PSZ(N = 38) and nonpsychiatric control subjects (NCS, N = 35) in a task that was designed to stress top-down control by pitting task goals against bottom-up salience. Participants searched for either a low-contrast (nonsalient) or a high-contrast (salient) target among low- and high-contrast distractors. By examining fixations of the low- and high-contrast items, we evaluated the ability of PSZ and NCS to focus on low-salience targets and filter out high-salience distractors (or vice versa). When participants searched for a salient target, both groups successfully focused on relevant, high-contrast stimuli and filtered out target-mismatched, low-contrast stimuli. However, when searching for a nonsalient target, PSZ were impaired at efficiently suppressing high-contrast (salient) distractors. Specifically, PSZ were more likely than NCS to fixate and revisit salient distractors, and they dwelled on these items longer than did NCS. The results provide direct evidence that PSZ are impaired in their ability to utilize top-down goals to overcome the prepotent tendency to focus attention on irrelevant but highly salient information. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Bistatic cylindrical millimeter-wave imaging for accurate reconstruction of high-contrast concave objects.

A high-contrast target with complex shape, especially concave surfaces, often exhibits strong high-order scattering during forward propagation, which is often misinterpreted as artifacts or phantom targets during imaging. In this work, a bistatic imaging method for reducing artifacts caused by high-order scattering from concave objects under cylindrical millimeter-wave scanning geometry is proposed. The effects of multiple reflections within concave structures are firstly analyzed by using ray-tracing techniques. It is observed that these troublesome multiple reflection echoes are often confined in limited scattering angles. Under this specific requirement for transceiver setup, a bistatic cylindrical aperture synthesis technique is proposed to obtain accurate images of concave object despite strong high-order scattering. To verify this method, simulated imaging results in bistatic near-field cylindrical imaging geometry are presented. Finally, the effectiveness of artifact reduction is confirmed by experimental results of complex metallic targets with concave outlines.

Analysis of The Performance of The NOSM For High Contrast Targets

Electromagnetic inverse scattering from high con-trast scatterers is of special importance, especially in MicrowaveImaging, wherein the recent technology aims to image highcontrast scatterer. To this purpose, this paper presents an analysisof the performance of the recently proposed microwave imagingtechnique of the Near Field Orthogonality Sampling (NOSM)for the high contrast targets. For this purpose, the indicator ofthe NOSM, which is the reduced scattered field, is derived foran electrically homogeneous circular scatterer, which is centeredaround origin. Next, the ratios of the indicator energy densitiesinside and outside of the target is defined as a quality metric.After, the quality metric and its expressions for limiting cases(i.e. where the electrical parameter of the the target is toolarge or too low) are derived in terms of the background’sand target’s electrical properties. Then, the introduced metricis computed and plotted for a popular application, which ismicrowave imaging of the breast. Obtained results show thatthe high contrasts between the target and the background doesnot have an important affect on the quality of the reconstructionsof the NOSM.