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(a) Surface composed of the microfacets in the XYZ coordinate system, (b) the schematics of a single microfacet.
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Compared with the standard depolarization index, indices of polarimetric purity (IPPs) have better performances to describe depolarization characteristics of targets with different roughnesses of interfaces under different incident angles, which allow us a further analysis of the depolarizing properties of samples. Here, we use IPPs obtained from d...
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Context 1
... larger the variance, the rougher the surface. In addition, rough surface is assumed to be made of many microfacets, called microfacets theory, and the normal vector of each microfacet can be determined by θ and í µí¼ shown in Figure 1b, which can be calculated by sampling [34]. Then, the reflective light and refractive light of microfacets can be calculated by the Fresnel formula and normal vector of microfacet [48,49]. ...
Context 2
... the reflective light and refractive light of microfacets can be calculated by the Fresnel formula and normal vector of microfacet [48,49]. In the tracking process of the polarized light, each beam of light propagates in global coordinate system shown in Figure 1a, and Fresnel's law is used in the local coordinate system defined by normal vector of microfacets shown in Figure 1b. Thus, it is necessary to translate two kinds of coordinate system. ...
Context 3
... the reflective light and refractive light of microfacets can be calculated by the Fresnel formula and normal vector of microfacet [48,49]. In the tracking process of the polarized light, each beam of light propagates in global coordinate system shown in Figure 1a, and Fresnel's law is used in the local coordinate system defined by normal vector of microfacets shown in Figure 1b. Thus, it is necessary to translate two kinds of coordinate system. ...
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Citations
... Polarization information is more sensitive to surface roughness, 3D shapes [29] and target material [30]. Therefore, polarization is widely used in the field of imaging [31][32][33][34]. ...
High-level detection of weak targets under bright light has always been an important yet challenging task. In this paper, a method of effectively fusing intensity and polarization information has been proposed to tackle this issue. Specifically, an attention-guided dual-discriminator generative adversarial network (GAN) has been designed for image fusion of these two sources, in which the fusion results can maintain rich background information in intensity images while significantly completing target information from polarization images. The framework consists of a generator and two discriminators, which retain the texture and salient information as much as possible from the source images. Furthermore, attention mechanism is introduced to focus on contextual semantic information and enhance long-term dependency. For preserving salient information, a suitable loss function has been introduced to constrain the pixel-level distribution between the result and the original image. Moreover, the real scene dataset of weak targets under bright light has been built and the effects of fusion between polarization and intensity information on different weak targets have been investigated and discussed. The results demonstrate that the proposed method outperforms other methods both in subjective evaluations and objective indexes, which prove the effectiveness of achieving accurate detection of weak targets in bright light background.
... Note that this is a fundamental result providing which phenomenon originates and/or dominates sample depolarization. In this vein, the association of physical mechanisms leading to depolarization with particular values of IPP provides further description, categorization, and recognition of depolarizing systems, these being very common in both biologic and synthetic materials [6,13,[23][24][25]43,44]. More importantly, this intrinsic microscopic information of samples can be obtained from very feasible macroscopic measurements (measure of Mueller matrix β=0.8 (c). ...
... Fortunately, there always exists difference in polarization characteristics between the targets and the backgrounds. Moreover, polarized lights also have good anti-scattering effects [22][23][24][25]. Therefore, polarization computational ghost imaging provides additional information for contrast-enhanced target recognition in traditional ghost imaging [26][27][28][29]. ...
Polarization computational ghost imaging (PCGI) often requires a large number of samples to reconstruct the targets, which can be optimized by reducing sampling rates with the aids of deep-learning technology. In this paper, the random patterns and successive orthonormalization instead of common Hadamard patterns, has been introduced into the deep-learning based PCGI system to recover high-quality images at lower sampling rates. Firstly, we use a polarized light to illuminate the target with random patterns for sampling. Then we can obtain a vector of bucket detector values containing the reflective information of the target. Secondly, we orthonormalize the vector according to the random patterns. Subsequently, the orthonormalized data can be input into the Improved U-net (IU-net) for reconstructing the targets. We demonstrate that higher-quality image of the testing sample can be obtained at a lower sampling rate of 1.5%, and superior-generalization ability for the untrained complex targets can be also achieved at a lower sampling rate of 6%. Meanwhile, we have also investigated the generalization ability of the system for the untrained targets with different materials that have different depolarization properties, and the system still demonstrates superior performances. The proposed method may pave a way towards the real applications of the PCGI.
... Computational ghost imaging (CGI) [1,2] is a considerable improvement over GI [3,4], where the detector on the reference arm can be omitted, simplifying the optical path and improving the performance of the GI. After that, high-order correlation imaging [5], differential CGI [6], and normalization CGI [7] have been continuously proposed, which enrich the experimental scheme of CGI and widely extend applications of CGI in terahertz imaging [8], three-dimensional imaging [9,10], imaging through atmospheric turbulence environment [11,12], polarization imaging [13][14][15][16], and so on. ...
Computational ghost imaging (CGI) can reconstruct scene images by two-order correlation between sampling patterns and detected intensities from a bucket detector. By increasing the sampling rates (SRs), imaging quality of CGI can be improved, but it will result in an increasing imaging time. Herein, in order to achieve high-quality CGI under an insufficient SR, we propose two types of novel sampling methods for CGI, to the best of our knowledge, cyclic sinusoidal-pattern-based CGI (CSP-CGI) and half-cyclic sinusoidal-pattern-based CGI (HCSP-CGI), in which CSP-CGI is realized by optimizing the ordered sinusoidal patterns through “cyclic sampling patterns,” and HCSP-CGI just uses half of the sinusoidal pattern types of CSP-CGI. Target information mainly exists in the low-frequency region, and high-quality target scenes can be recovered even at an extreme SR of 5%. The proposed methods can significantly reduce the sampling number and real-time ghost imaging possible. The experiments demonstrate the superiority of our method over state-of-the-art methods both qualitatively and quantitatively.
... In general, fully polarized light gets depolarized when it transmits through a scattering environment [31,32]. While DOP of a fully polarized light is 1 and is 0 for unpolarized light, DOP for a partially ...
Understanding the virology of the coronavirus at the structural level has gained utmost importance to overcome the constant and long-term health complications induced by them. In this work, the light scattering properties of SARS-CoV-2 of size 140 nm were simulated by using discrete dipole approximation (DDA) for two incident wavelengths 200 nm and 350 nm, respectively. Three different 3-dimensional (3D) models of SARS-CoV-2 corresponding to 15, 20, and 40 numbers of spike proteins on the viral capsid surface were constructed as target geometries for the DDA calculations. These models were assessed by employing Stokes–Mueller polarimetry to obtain individual polarization properties such as degree of polarization (DOP), degree of linear polarization (DOLP), and degree of circular polarization (DOCP). Irrespective of its spike numbers, all the coronavirus models were found to display higher DOP and DOCP values and negligibly small DOLP values for circularly polarized incident light, indicating the presence of chiral structures. On the other hand, the lack of understanding about the dependence of the Mueller matrix on its microstructural properties was overcome by transforming 16 Mueller elements into sub-matrices with specific structural and physical properties using Lu–Chipman-based Mueller matrix polar decomposition method. The obtained properties such as retardance, diattenuation, and depolarization were used for investigating the composition and microstructural information. The approach presented in this work has the potential to understand the virology of the coronavirus at the structural level and, therefore, will be beneficial in developing effective detection strategies by exploiting their characteristic electromagnetic scattering signatures.
... LI et al. described the glucose concentrations changes in the scattering system by polarization imaging technology [26]. They used indices of polarimetric purity obtained from different reflective interfaces as a criterion of depolarization property to characterize and classify targets covered by organic paint layers with different roughness [27]. They introduced a reflective polarization based computational ghost imaging system based on a Monte Carlo model under the foggy environments [28]. ...
... In the I (α, β ), α is the angle of polarization, and β stands for phase delay. We can calculate the depolarization of the scattered light P, (27) According to P, the target material can be judged [7]. ...
... Polarization imaging is an important method for target recognition [25][26][27][28][29]. Lidar is different from polarization imaging. ...
Laser detection devices obtain target information from back-scattered light, such as lidar. The recognition rate can be improved by analyzing intensity and polarization of echo signal. In this paper, Monte Carlo method is used to generate a large number of randomly rough surfaces to simulate targets. Every rough surface is discretized into a large number of micro-surface elements. Stokes parameters of back-scattered light are calculated by numerical integration. Incident light is p-, s-, 45° linearly polarized light and right-hand circularly polarized light, respectively. Numerical results show that when s- and p-linearly polarized light incident on a metal rough surface, back-scattered light appears circularly polarized component. Metal rough surface resembles a wave plate with phase difference, with the fast axis parallel or perpendicular to the 45° direction. When linearly polarized light is incident on dielectric rough surface, back-scattered light has no circularly polarized component. Experimental data are consistent with the numerical results. The above research provides a new basis for laser detection device to identify metal targets from the environmental background.
... Polarization [20][21][22][23][24] as an intrinsic characteristic of electromagnetic wave has been proven to be a powerful tool to characterize targets [25][26][27][28], which can obtain more-dimension information of targets. Thus, polarization has been widely used to improve the ability of detecting and reconstructing the object from complex scenes. ...
Computational ghost imaging (CGI) uses preset patterns and single-pixel detection, breaking through the traditional form of point-to-point imaging. In this paper, based on the Monte Carlo model, a reflective polarization based CGI (PCGI) system has been proposed and constructed under the foggy environments. And the imaging performances of the PCGI at different optical distances have been investigated and analyzed quantitatively. When the targets and the background have a small difference in reflectivity, the difference of polarization characteristics between the targets and the background can help the CGI to remove the interference of scattering light and improve the imaging contrast. Besides, in order to further improve imaging efficiency, a scanning-mode polarization based CGI (SPCGI) has also been proposed, in which the combination of polarization characteristics and the scanning-mode plays an important role to improve the CGI’s imaging efficiency and imaging quality.
... The fundamental understanding of light scattering phenomena is of interest in scientific and technological fields as diverse as astronomy, remote sensing, metrology and biomedical imaging, among others [1][2][3][4][5][6][7][8][9][10][11][12][13]. In particular, the study of light depolarization processes finds a very wide scope of applications, for instance, the characterization of gaseous structures in the cosmos [3,4], remote sensing of diffuse objects [1], and the early diagnosis of diseases [5,8,9]. ...
... This example shows how IPP are useful to distinguish among different types of anisotropic depolarizers while CP are not able to discriminate these depolarizers because all the elements are retarders without any dichroism, an essential characteristic of the P-D-P S space. It is worth noting that scattering systems with different intrinsic properties (particle's size, particle's density, refractive index, etc.) presents different depolarization anisotropies, so they are described by different IPP [10][11][12][13]. Accordingly, IPP can be used to characterize such dispersion systems effectively, as proved in the literature [10][11][12][13]. ...
... It is worth noting that scattering systems with different intrinsic properties (particle's size, particle's density, refractive index, etc.) presents different depolarization anisotropies, so they are described by different IPP [10][11][12][13]. Accordingly, IPP can be used to characterize such dispersion systems effectively, as proved in the literature [10][11][12][13]. In fact, some works showed that the variation of these intrinsic properties of the system show different trajectories in the IPP space [10,11] so this 3D representation (Fig. 2) is an ideal tool for the recognition and classification of scattering media. ...
The link between depolarization measures and physical nature and structure of material media inducing depolarization is nowadays an open question. This article shows how the joint use of two complementary sets of depolarizing metrics, namely the Indices of polarimetric purity and the Components of purity, are sufficient to completely describe the integral depolarizing properties of a sample. Based on a collection of illustrative and representative polarimetric configurations, a clear and meaningful physical interpretation of such metrics is provided, thus extending the current tools and comprehension for the study and analysis of the depolarizing properties of material media. This study could be of interest to those users dealing with depolarization or depolarizing samples.
... Diffuse reflected light emitted from a target's surface can help reconstruct the 3D shape of the target surface based on the polarization reflectance model [22][23][24]. However, the surface reflectance of diffuse reflection components due to the color is a key factor influencing the reconstruction results [25]. ...
This paper presents a near-infrared (NIR) monocular 3D computational polarization imaging method to directly reconstruct the shape of surfaces exhibiting nonuniform reflectance. A reference gradient field is introduced to the weight constraints for globally correcting the ambiguity of the surface normal for a target with nonuniform reflectance. We experimentally demonstrated that our method can reconstruct the shape of surfaces exhibiting nonuniform reflectance in not only the near field but also the far field. Moreover, with the proposed method, the axial resolution can be kept constant even under different object distances as long as the ratio of the focal length to the object distance is fixed. The simplicity and robustness of the proposed method make it an attractive tool for the fast modeling of 3D scenes.
... As one of the intrinsic properties of light, in recent years, the polarization state of the light has attracted a lot of attentions due to its great application potentials in communications [1][2][3], navigation [4,5] and detection [6][7][8]. In general, the absorption and scattering of light by complex dispersion media lead to the loss of target information and serious degradation of image quality. ...
Polarization imaging is a powerful tool, which can be applied in biomedical diagnosis and many research fields. Here, we propose a new application of the indices of polarimetric purity (IPPs) composed of P1, P2, P3, to describe the glucose concentrations (GC) changes in the scattering system. The results suggest that P1 of the IPPs is a better indicator to GC in the solution than the degree of polarization (DoP) for the forward scattering detection. Meanwhile, the fitting relation among radius of scattering particle, GCs and P1 parameter has also been calculated, in which the error of inversion is no more than 4.73%. In the backscattering detection, the fitted frequency statistical histogram of the IPPs is used to measure the GCs, and their modes can represent changing trend of GCs.
