Reflection Component Research Articles

Nighttime image semantic segmentation with retinex theory

Nighttime image semantic segmentation is challenging due to low-light and diverse lighting conditions. A straightforward solution is to first enhance nighttime scene images to resemble daytime scene before performing segmentation. This kind of methods heavily rely on the enhancement quality. Inspired by the Retinex theory for low-light image enhancement, which decomposes an image into reflectance and illumination components, we propose a novel nighttime image segmentation method with Retinex theory (RNightSeg). Our core insight is to obtain high-quality illumination-independent reflectance component to enhance segmentation. Specifically, we apply a decomposition decoder to the backbone network for generating the reflectance component. In addition to the fidelity loss and Total Variation loss for the reflectance component regression, we model the brightening illumination component to enhance the nighttime image and apply the color constancy loss on the enhanced image. This helps to cope with the issue of low-light and diverse lighting in the nighttime scene. Finally, we fuse the reflectance decoder feature with the backbone feature and feed the fused feature to the segmentation decoder. Extensive experimental results on two widely used datasets demonstrate that the proposed RNightSeg achieves superior performance over some state-of-the-art segmentation methods. The code of our implementation is available at https://github.com/sunzc-sunny/RNightSeg.

Forest 3D Radar Reflectivity Reconstruction at X-Band Using a Lidar Derived Polarimetric Coherence Tomography Basis

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis.

74‐1: Display Reflectance Measurements Finally Made Simple, Comprehensive and Affordable

Analysis of the point‐spread function (PSF) yields the directional variations of scattered light with high resolution in the vicinity of the specular direction and now, finally, at reduced but sufficient resolution, also at off‐specular directions, thus providing a basis for the determination of a set of complete in‐plane BRD/BTD functions. This approach is applied to both identification and separation of mirror, haze and Lambertian reflection components which then can be classified into such components that are forwarding visual information to the observer (i. e. visual signal) and disturbing reflections (i.e. visual noise).

74‐3: Regular Reflectance and Transmittance Measured by the Annulus Source Method

Prior work showed how the annulus source method could extract the true regular reflectance component from a reflective display in the presence of multiple scattering components. This study extends this work by demonstrating the capability of this method to measure the regular reflectance and transmittance of emissive, transparent, and augmented reality displays.

A Retinex-based network for image enhancement in low-light environments.

Most of the existing low-light image enhancement methods suffer from the problems of detail loss, color distortion and excessive noise. To address the above-mentioned issues, this paper proposes a neural network-based low-light image enhancement network. The network is divided into three parts: decomposition network, reflection component denoising network, and illumination component enhancement network. In the decomposition network, the input image is decomposed into a reflection image and an illumination image. In the reflection component denoising network, the Unet3+ network improved by fusion CA attention is adopted to denoise the reflection image. In the illumination component enhancement network, the adaptive mapping curve is adopted to enhance the illumination image iteratively. Finally, the processed illumination and reflection images are fused based on Retinex theory to obtain the final enhanced image. The experimental results show that the proposed network achieves excellent visual effects in subjective evaluation. Additionally, it shows a significant improvement in objective evaluation metrics, including PSNR, SSIM, NIQE, and so on, when compared to the results in several public datasets.

Improvement of anticorrosion coating thickness measurement using multi-wavelength lock-in infrared data processing

Corrosive environments deteriorate coated steel plates and the corrosion protection of the coatings is not permanent. The infrared spectral characteristics in various infrared wavelength ranges vary with the degradation state of a coated steel plate. To monitor the deterioration state of coated steel plates by infrared measurements, establishing an inspection method to remotely and effectively evaluate the deterioration state of the coating in various infrared wavelength ranges is necessary. However, mid-wavelength infrared cameras capture both the infrared reflection from the surface of the coating and the temperature rise of the coating. These two components cancel each other and affect the detection of coating deterioration. Thus, this study proposes a multi-wavelength lock-in infrared data processing method that performs lock-in processing using time-series data of only the infrared reflection or emission components at different infrared wavelength ranges as the reference signal. The multi-wavelength lock-in infrared data processing can extract infrared reflection and emission components separately from the mid-wavelength infrared region. The proposed method could quantitatively evaluate the thickness of the coating for each infrared component.

DAF‐Retinex: Preserve the image detailed features and restore the reflected image

AbstractCurrently, deep learning methods for low‐light image enhancement tasks mainly focus on the illumination of images, while neglecting the problems of image noise and feature loss. To address this issue, this paper proposes a novel low‐light image enhancement network called DAF‐Retinex, based on the Retinex‐Net. To address the issue of image noise, different from traditional image denoise methods, this paper utilizes a fully convolutional neural network to denoise the reflection component, additionally, a denoising loss function is introduced to suppress noise. For preserving image details and extracting features, this paper creatively introduces self‐calibrated convolutions into low‐light image enhancement tasks, furthermore, a feature augmented attention block consisting of feature‐guided attention (FGA) is designed for feature learning to effectively enhance image illumination and extract image detail features. Experimental results demonstrate that the proposed algorithm in this paper effectively removes image noise and extracts detailed features, resulting in visually improved outcomes. On public datasets, the average improvement in objective evaluation metrics of image quality such as PSNR, SSIM, and NIQE are 1.13%, 4.12%, and 1.28%, respectively.

Racism and Mental Health: The Moderating Role of Critical Consciousness for Black Adolescents

This study examined experiences of individual, institutional, and cultural racism, along with critical consciousness (i.e., critical reflection, critical agency, critical action), and how they are associated with mental health outcomes for Black adolescents (N = 604; Mage = 15.4; 47.4% female). Consistent with previous research, we found that more experiences of racism were associated with more mental health distress for Black adolescents. We also found that the relationship between racism and mental health varied by critical reflection and critical action, in a three-way interaction effect. The positive association between racism and mental health distress was weaker for the Black adolescents in our sample who reported higher than average critical reflection and lower than average critical action. This evidence suggests that the reflection and action components of critical consciousness, together, can serve as an adaptive coping strategy to guard against the harm racism can cause to mental health. Black adolescents experience less mental health distress when they have a deep understanding of oppression, but do not engage heavily in actions to dismantle those unjust systems. These findings have implications for how youth researchers and practitioners can support critical consciousness development in ways that do not compromise adolescent mental health.

Novel Illumination-invariant Face Recognition Approach via Reflectance-luminance and Local Matching Model with Weighted Voting System

In this study, a novel approach has been introduced for face recognition that is unaffected by changes in illumination. This method is based on the reflectance-luminance model and incorporates local matching using a weighted voting technique to eliminate any artifacts present in the retina images. A total of 37 different linear and nonlinear filters were tested, including high pass and low pass filters, to achieve the reflectance component of the image, which remains invariant to changes in illumination. Among these filters, the maximum filter, which is a simple filter with low computational complexity, yielded the best results in extracting the illumination invariants. It was observed that the illumination invariants obtained through this method outperformed other methods such as QI, SQI, and image enhancement techniques in terms of recognition accuracy. Importantly, the proposed method does not require any prior knowledge about the facial shape or illumination conditions and can be applied to individual images independently. Unlike many existing methods, this approach does not rely on multiple images during the training stage and does not require any parameter selection to generate the illumination invariants. To further enhance the robustness of illumination, a weighted voting system was introduced. Certain regions of the image, which may adversely affect the recognition outcome due to poor illumination, occlusion, noise, or lack of distinctive information, were identified using predefined factors such as grayscale mean, image entropy, and mutual information. The proposed method was also compared to other face recognition methods in the presence of occlusions, and it demonstrated promising results outperforming existing methods. The Python algorithm successfully detects obstructed faces, genders, and ages in videos with a face matching accuracy between 80.9% to 96.9% based on proximity.

A low-light image enhancement model based on anisotropic weighted exponential local derivatives

Low-light images incur several complicated degradations such as low brightness, poor contrast, and higher noise. Retinex theory, which decomposes a low-light image into illumination and reflection components based on local image derivatives, is widely used in the field of shimmering image enhancement. In this paper, we propose a new low-light image enhancement model based on the Retinex theory to reduce the above distortions. To be more specific, we first introduce a new structural texture extractor (NSTE) based on the local image derivatives properties to generate the texture and structure maps of the observed images. Compared to existing structural texture extractors, NSTE has two key designs: anisotropic weighting and adaptive exponential term. The aim is to generate structural maps with clear edge and texture maps with salient details. Subsequently, these two images are used to regularize the illumination and reflection components of the variational decomposition model, respectively. Finally, we developed a luminance correction function and a detail enhancement function to enhance the illumination and reflectance, respectively. This aims to improve the viewing experience, mitigate over-enhancement, and minimize detail distortion. We choose the alternating direction method of multipliers (ADMM) to solve the optimization model. Experimental results on several low-light datasets demonstrate that our proposed method can achieve better quantitative and qualitative performance than state-of-the-art methods.

Probing the stability and quality of the cellulose-based Pickering emulsion containing sesamolin-enriched sesame oil by chemometrics-assisted ATR-FTIR spectroscopy

This study presents the employment of Fourier transform infrared (FTIR) spectroscopy with attenuated total reflection and principal component analysis (PCA) to analyze the stability of a Pickering emulsion stabilized by carboxylated-cellulose nanocrystal (cCNC) comprising sesame oil phases with or without sesamolin. FTIR measurements identified an intermolecular hydrogen bond between the ester group of the triglyceride and the carboxyl group of the cCNC to create the emulsion droplet. The spectral bands from the hydroxyl group vibration (3700–3050 cm−1), carbonyl (1744 cm−1), CO groups of the ester triglyceride and cCNC (1160–998 cm−1) markedly discriminated between stabilized and destabilized emulsions. The PCA of FTIR spectra detected the change of molecular interaction during storage according to creaming, aggregation, and coalescence and changes in physicochemical parameters such as droplet size, refractive index, and zeta potential. Hence, PCA enabled the observation of the destabilization of emulsion in real-time.

The bright black hole X-ray binary 4U 1543–47 during the 2021 outburst: A thick accretion disk inflated by high luminosity

The black hole X-ray binary source 4U 1543–47 experienced a super-Eddington outburst in 2021, reaching a peak flux of up to ∼1.96 × 10−7 erg cm−2 s−1 (∼8.2 Crab) in the 2−10 keV band. Soon after the outburst began, it rapidly transitioned into the soft state. Our goal is to understand how the accretion disk structure deviates from a standard thin disk when the accretion rate is near Eddington. To do so, we analyzed spectra obtained from quasi-simultaneous observations conducted by the Hard X-ray Modulation Telescope (Insight-HXMT), the Nuclear Spectroscopic Telescope Array (NuSTAR), and the Neil Gehrels Swift Observatory (Swift). These spectra are well fitted by a model comprising a disk, a weak corona, and a reflection component. We suggest that the reflection component is caused by disk self-irradiation, that is by photons emitted from the inner disk that return to the accretion disk surface as their trajectories are bent by the strong gravity field. In this scenario, the best-fitting parameters imply that the reflected flux represents more than half of the total flux. Using general relativistic ray-tracing simulations, we show that this scenario is viable when the disk becomes geometrically thick, with a funnel-like shape, as the accretion rate is near or above the Eddington limit. In the specific case of 4U 1543–47, an angle ≳45 deg between the disk surface and the equatorial plane can explain the required amount of self-irradiation.

Enhancing Relapse Prevention: Examining the Impact of Experiential Avoidance, Integrative Self-Knowledge, and Basic Psychological Needs in Substance Use Treatment.

The harmful effects of drug relapse have always been one of the major challenges in addiction treatment. The present study aimed to predict drug relapse in addicted men under treatment based on experiential avoidance, integrative self-knowledge, and basic psychological needs. The present study was a correlational one. The statistical population included all addicted men in Choubindar prison in Qazvin in 2021, among whom 200 individuals were selected randomly. Then, the participants filled out the Relapse Prediction Scale (RPS), Multidimensional Experiential Avoidance Questionnaire (MEAQ), Integrative Self-Knowledge Scale (ISK), and Basic Psychological Needs Scale (BPNS). Data were analyzed using stepwise regression via SPSS software (version 25). The results of the study demonstrated that some of the components of experiential avoidance including distraction, distress endurance, behavioral avoidance, and distress aversion could account for 14.0% of the variance of the relapse in the addicts (P<0.05). Moreover, the obtained results considering the reflective self-knowledge component and the overall score of integrative self-knowledge could explain 15.0% of the variance in relapse in the addicts. Among the basic psychological needs, communication could predict 3.8% of the variance in relapse. Based on the results of the present study, it is suggested that through addiction treatment and prevention of relapse programs, psychologists reduce drug relapse in addicts by decreasing distractions and behavioral avoidance, increasing distress endurance, enhancing self-knowledge, and improving efficient relationships.

Factor Model of Life-Planning Competence in High School Students

Life-planning competence is an integral personality trait that characterizes one’s desire and ability to implement one’s potential, i.e., knowledge, skills, experience, character traits, etc., in order to achieve one’s life objectives. It consists of motivational, cognitive, emotional, volitional, axiological, semantic, operational, reflective, and proactive components. The present research featured the life-planning competence in high school students (n = 200; 2019–2022) of the city of Krasnoyarsk, Russia. The resulting strategies demonstrated a connection with the concept of being-inthe-world by M. Heidegger and L. Binswanger, as well as A. Längle’s concept of fundamental existential motivations. The statistical analysis involved the principal component method with Varimax rotation and Kaiser sampling. The factor analysis revealed eight factors: 1) purposefulness, meaningfulness, and readiness for life planning; 2) cooperation; 3) reflexive activity control; 4) correlation with the world; 5) control of oneself and the situation; 6) self-awareness; 7) dynamic balance; 8) self-development. These factors connected the process of life-planning with the four existential fundamental motivations identified by A. Längle and the forms of being-in-the-world described by M. Heidegger and L. Binswanger. The high school students demonstrated a motivation for physical survival and spiritual overcoming of existence, as well as a desire to enjoy life, experience values, find self-identity, and seek justice. As teenagers, they lacked the fourth existential motivation, i.e., motivation to obtain existential meaning.

Estimating intrinsic characteristics of images for shadow removal

Existing works show that shadow removal tasks can benefit from the physical illumination model on the formation of shadows. Inspired by prior works that recover the intrinsic characteristics by decomposing an image for its reflectance and illumination components, we study a variant of shadow illumination model that can better reflect the complexity in the real world — in this model, shadow-free pixels can be expressed by a translation formed of reflectance and illumination components. Based on the new illumination model, we develop a new LR-ShadowNet, which contains two sub-nets for estimating the illumination and reflectance components, respectively, and one sub-net for refining the shadow-removal result. Besides, several mask guidance module are incorporated into LR-ShadowNet for guiding components estimation and result refinement based on shadow region information. The whole network is trained in an end-to-end fashion guided by the shadow masks. Extensive experiments on the ISTD dataset and SBU-Timelapse dataset show that the proposed LR-ShadowNet achieves competitive performance with less computational cost and strong generalisation ability.

The Impact of a Dual Education System on the Professional Training of Future Preschool Teachers in Higher Education Institutions

The aim of the article is to determine the specifics of the impact of dual education on the future preschool teachers’ professional competence. The study employed such methods as testing, observation, and formative experiment. It was established that future preschool teachers are characterized by low motivation for professional activity, medium level of theoretical knowledge and professional skills, insufficiently developed pedagogical reflection. The hypothesis about the difference between the influence of dual and institutional education on the future preschool teachers’ professional competence was partially confirmed. Dual education demonstrated the best effectiveness in developing the active and reflective components in future preschool teachers. They are slightly better in the development of indicators of the cognitive component, the results belong to the institutional form of higher education. The dual education programme is ineffective in developing the motivational component of the future preschool teachers’ professional competence. The obtained results open up opportunities to improve dual education programmes for the training of future preschool teachers: focus on the development of future preschool teachers’ professional motivation, improving theoretical knowledge in a preschool education institution. The results also indicate the need to enhance the development of motivation and professional reflection of future specialists in the institutional form of higher education. The prospects for further research are the study of the possibilities of using dual education in the training of students of other pedagogical majors.

Self-supervised monocular depth estimation on water scenes via specular reflection prior

Monocular depth estimation from a single image is an ill-posed problem for computer vision due to insufficient reliable cues as the prior knowledge. Besides the inter-frame supervision, namely stereo and adjacent frames, extensive prior information is available in the same frame. Reflections from specular surfaces, informative intra-frame priors, enable us to reformulate the ill-posed depth estimation task as a multi-view synthesis. This paper proposes the first self-supervision for deep-learning depth estimation on water scenes via intra-frame priors, known as reflection supervision and geometrical constraints. In the first stage, a water segmentation network is performed to separate the reflection components from the entire image. Next, we construct a self-supervised framework to predict the target appearance from reflections, perceived as other perspectives. The photometric re-projection error, incorporating SmoothL1 and a novel photometric adaptive SSIM, is formulated to optimize pose and depth estimation by aligning the transformed virtual depths and source ones. As a supplement, the water surface is determined from real and virtual camera positions, which complement the depth of the water area. Furthermore, to alleviate these laborious ground truth annotations, we introduce a large-scale water reflection scene (WRS) dataset rendered from Unreal Engine 4. Extensive experiments on the WRS dataset prove the feasibility of the proposed method compared to state-of-the-art depth estimation techniques.

A Multiwavelength Study of the Hard and Soft States of MAXI J1820+070 During Its 2018 Outburst

We present a comprehensive multiwavelength spectral analysis of the black hole (BH) X-ray binary MAXI J1820+070 during its 2018 outburst, utilizing AstroSat far-UV, soft X-ray, and hard X-ray data, along with (quasi-)simultaneous optical and X-ray data from the Las Cumbres Observatory and NICER, respectively. In the soft state, we detect soft X-ray and UV/optical excess components over and above the intrinsic accretion disk emission (kT in ∼ 0.58 keV) and a steep X-ray power-law component. The soft X-ray excess is consistent with a high-temperature blackbody (kT ∼ 0.79 keV), while the UV/optical excess is described by UV emission lines and two low-temperature blackbody components (kT ∼ 3.87 and ∼0.75 eV). Employing continuum spectral fitting, we determine the BH spin parameter (a = 0.77 ± 0.21), using the jet inclination angle of 64° ± 5° and a mass spanning 5–10 M ☉. In the hard state (HS), we observe a significantly enhanced optical/UV excess component, indicating a stronger reprocessed emission in the outer disk. Broadband X-ray spectroscopy in the HS reveals a two-component corona, each associated with its reflection component, in addition to the disk emission (kT in ∼ 0.19 keV). The softer coronal component dominates the bolometric X-ray luminosity and produces broader relativistic reflection features, while the harder component gets reflected far from the inner disk, yielding narrow reflection features. Furthermore, our analysis in the HS suggests a substantial truncation of the inner disk (≳51 gravitational radii) and a high disk density (∼1020 cm−3).

X-ray measurement of a high-mass white dwarf and its spin for the intermediate polar IGR J18434−0508

ABSTRACT IGR J18434−0508 is a Galactic Intermediate Polar (IP) type Cataclysmic Variable (CV) previously classified through optical spectroscopy. The source is already known to have a hard Chandra spectrum. In this paper, we have used follow-up XMM–Newton and NuSTAR observations to measure the white dwarf (WD) mass and spin period. We measure a spin period of P = 304.4 ± 0.3 s based on the combined MOS1, MOS2, and pn light curve. Although this is twice the optical period found previously, we interpret this value to be the true spin period of the WD. The source has an $8\pm 2~{{\ \rm per\ cent}}$ pulsed fraction in the 0.5–10 keV XMM–Newton data and shows strong dips in the soft energy band (0.5–2 keV). The XMM–Newton and NuSTAR joint spectrum is consistent with a thermal bremsstrahlung continuum model with an additional partial covering factor, reflection, and Fe-line Gaussian components. Furthermore, we fit the joint spectrum with the post-shock region ‘ipolar’ model which indicates a high WD mass ≳1.36 M⊙, approaching the Chandrasekhar limit.

Fractional structure and texture aware model for image Retinex and low-light enhancement

This paper proposes a fractional structure and texture aware Retinex (FSTAR) model for image decomposition with application to low-light enhancement. First, a novel structure aware measure called Maximum Fractional Difference (MFD) is introduced, which is the maximum of fractional differences of the input image in eight symmetric directions. Then fractional structure and texture aware maps are built based on MFD and Huber function from robust statistics, which are used as weighted matrices in the regularization terms of reflectance and illumination components. To ensure the intrinsic physical constraints of Retinex on two components, two extra penalty terms are incorporated into the objective function of FSTAR to penalize deviations of the estimated components from the corresponding constraints. The FSTAR is solved via an alternating iterative algorithm; i.e., the objective function is minimized with respect to one variable with another variable fixed, and this process is done alternately until termination condition is met. Qualitative and quantitative evaluations of FSTAR on three low-light image datasets, compared to ten state-of-the-art methods, show its superior performance in Retinex decomposition and low-light image enhancement.