Shadow Images Research Articles

Imaging ultracompact objects with radiatively inefficient accretion flows

Context. Recent Event Horizon Telescope observations of M 87* and Sgr A* strongly suggest the presence of a supermassive black hole at their respective cores. We use the semi-analytic radiatively inefficient accretion flows (RIAF) model to investigate the resulting images of the Joshi–Malafarina–Narayan (JMN-1) naked singularity and the Schwarzschild black hole. Aims. We chose the JMN-1 naked singularity model and compared the synchrotron images with the Schwarzschild solution to search for any distinct features that can distinguish the two objects and to find an alternative to the solution with a black hole. Methods. We performed general relativistic ray-tracing and radiative transfer simulations using the Brahma code to generate synchrotron-emission images using the thermal distribution function for emissivity and absorptivity. We investigated effects in the images by varying the inclination angle, the disk width, and the frequency. Results. The shadow images simulated with the JMN-1 model closely resemble those generated by the Schwarzschild black hole. The disparities between the two images are very small. We conducted simulations using various plasma parameters, but the resulting images remained largely consistent for both scenarios. This similarity is evident in the horizontal cross-sectional brightness profiles of the two scenarios. Notably, the JMN-1 model exhibits a slightly higher intensity than the Schwarzschild black hole. Conclusions. We conclude that JMN-1 is a viable substitute for the black hole scenario. This conclusion is not solely grounded in the fact that the two scenarios are indistinguishable from their respective shadow observations, but also in the consideration that JMN-1 emerges as an end state of a continual gravitational collapse. This paradigm not only allows for constraints on spacetime, but also provides a good probe for the nature of the central compact object.

Hyperspectral Image Shadow Enhancement Using Three-Dimensional Dynamic Stochastic Resonance and Classification Based on ResNet

Classification is an important means of extracting rich information from hyperspectral images (HSIs). However, many HSIs contain shadowed areas, where noise severely affects the extraction of useful information. General noise removal may lead to loss of spatial correlation and spectral features. In contrast, dynamic stochastic resonance (DSR) converts noise into capability that enhances the signal in a way that better preserves the image’s original information. Nevertheless, current one-dimensional and 2D DSR methods fail to fully utilize the tensor properties of hyperspectral data and preserve the complete spectral features. Therefore, a hexa-directional differential format is derived in this paper to solve the system’s output, and the iterative equation for HSI shadow enhancement is obtained, enabling 3D parallel processing of HSI spatial–spectral information. Meanwhile, internal parameters are adjusted to achieve optimal resonance. Furthermore, the residual neural network 152 model embedded with the convolutional block attention module is proposed to diminish information redundancy and leverage data concealed within shadow areas. Experimental results on a real-world HSI demonstrate the potential performance of 3D DSR in enhancing weak signals in HSI shadow regions and the proposed approach’s effectiveness in improving classification.

Synthetic Document Images with Diverse Shadows for Deep Shadow Removal Networks.

Shadow removal for document images is an essential task for digitized document applications. Recent shadow removal models have been trained on pairs of shadow images and shadow-free images. However, obtaining a large, diverse dataset for document shadow removal takes time and effort. Thus, only small real datasets are available. Graphic renderers have been used to synthesize shadows to create relatively large datasets. However, the limited number of unique documents and the limited lighting environments adversely affect the network performance. This paper presents a large-scale, diverse dataset called the Synthetic Document with Diverse Shadows (SynDocDS) dataset. The SynDocDS comprises rendered images with diverse shadows augmented by a physics-based illumination model, which can be utilized to obtain a more robust and high-performance deep shadow removal network. In this paper, we further propose a Dual Shadow Fusion Network (DSFN). Unlike natural images, document images often have constant background colors requiring a high understanding of global color features for training a deep shadow removal network. The DSFN has a high global color comprehension and understanding of shadow regions and merges shadow attentions and features efficiently. We conduct experiments on three publicly available datasets, the OSR, Kligler's, and Jung's datasets, to validate our proposed method's effectiveness. In comparison to training on existing synthetic datasets, our model training on the SynDocDS dataset achieves an enhancement in the PSNR and SSIM, increasing them from 23.00 dB to 25.70 dB and 0.959 to 0.971 on average. In addition, the experiments demonstrated that our DSFN clearly outperformed other networks across multiple metrics, including the PSNR, the SSIM, and its impact on OCR performance.

Semantic-aware Transformer for shadow detection

Shadow detection is significant for scene understanding. Ambiguities in a shadow image, such as shadow-like non-shadow regions and shadow regions with non-shadow patterns, are still very challenging for prevalent CNN-based methods. This work attempts to alleviate this problem from a new perspective of shape semantics, and then proposes a Semantic-aware Transformer (SaT) in a multi-task learning manner. Concretely, we first propose a shadow detection network based on the recent progress of Transformer architecture, allowing us to capture significant global interactions between contexts. Next, we design a multi-task learning framework, combining shadow supervision and semantic supervision to perform a semantic-aware shadow detection. Finally, we introduce a simple yet effective information buffer unit to overcome the gradient signal conflict from multi-task learning. Experimental results on three public benchmark datasets (i.e., ISTD, SBU, and UCF) show that our SaT can effectively detect ambiguous cases and achieve state-of-the-art results.

Polarization imaging shadow removal based on attention conditional generative adversarial networks

Polarization imaging shadow removal based on attention conditional generative adversarial networks

The size distribution of the agitated saline microbubbles for contrast transcranial Doppler generated using standard manual methods.

Agitated saline microbubbles (MBs) are a common contrast agent for determining right-to-left shunt (RLS) by the contrast transcranial Doppler (c-TCD). The size of the generated bubbles is not standardized in clinical practice. MBs were generated using the recommended manual method by reciprocating motion through two syringes. The bubble size distributions (BSD) were measured using the microscopic shadow imaging technique. The results show that the diameter of MBs is mainly distributed between 10 and 100 μm, the mean bubble size is between 21 and 34 μm, the Sauter mean diameter (D32) is primarily between 50 and 300 μm, and the standard deviation (SD) is between 6 and 17 μm in 80 experiments. It provides a more accurate basis for the recommended manual method instability. The high variance values of the BSD indicate that the manual method has low stability and repeatability. The results of this study can be useful for further improvement of the reliability of c-TCD in detecting RLS. RESEARCH HIGHLIGHTS: This study provided the first detailed descriptions of the MBs size distribution in a flowing contrast agent by the microscopic shadow imaging technique. It reveals significant differences in the bubble size of manual foaming during repeated manipulations for each individual and between individuals.

Verifiable visually meaningful image encryption algorithm based on compressive sensing and (t, n)-threshold secret sharing

Abstract A verifiable visually meaningful image encryption algorithm based on compressive sensing and (t, n)-threshold secret sharing is proposed. Firstly, the plain image is compressed and encrypted by 2D block compressive sensing to obtain the pre-encrypted image. During this process, the enhanced logistic map and the enhanced tent map are used to generate the measurement matrix and the permutation sequence, respectively. Secondly, multiple shadow images of the pre-encrypted image are generated by using the (t, n)-threshold secret sharing scheme, and then each shadow image is further encrypted by using the encryption matrix generated by the chaotic system. Meanwhile, the signatures of the shadow images are obtained by using RSA signature algorithm. Finally, the shadow images and their corresponding signatures are embedded into the carrier image to generate the cipher image with high visual quality by using the LSB method. Additionally, the present algorithm can resist known-plaintext and chosen-plaintext attacks by incorporating the hash value of the plain image as part of the key. Meanwhile, the use of 2D block compressive sensing significantly reduces the reconstruction time. Simulation results demonstrate that the proposed algorithm achieves excellent decryption quality and operational efficiency.

High-capacity medical information hiding scheme with P-tensor product secret image sharing for wireless body area networks

Given the escalating demand for information transmission in the resource-restricted wireless body area networks (WBANs), securing medical data and minimizing energy consumption are paramount. We present an innovative information hiding scheme that employs P-tensor product secret image sharing (PTP-SIS) and multiple information-hiders for WBANs. Considering the remarkable compression performance demonstrated by compressed sensing (CS), as well as its link to Shamir's (k, n) secret sharing method, using CS rather than traditional secret sharing techniques for image processing purposes could potentially help alleviate transmission burden of sensor nodes. However, utilizing CS to process high-dimensional signals poses a significant challenge due to the large dimensionality of the measurement matrix. The P-tensor product (PTP) is a tool that offers outstanding universality and can overcome the dimension limitations of traditional matrix multiplication, it is applied in the secret sharing stage. Moreover, applying the ideas of secret sharing leads to less stress on an individual device as well as lower communication spending, and multiple hiders have the ability to independently embed additional secrets into shadow images for efficient information backup. Theoretical analysis and experiments validate that the proposed scheme ensures information security and transmission efficiency while achieving superior image recovery performance and higher information embedding rates compared to other approaches.

Blockchain-Based Secure and Efficient Secret Image Sharing With Outsourcing Computation in Wireless Networks

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> shadow images in the way that any subset of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k, n</i> ) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks.

Shadows of loop quantum black holes: semi-analytical simulations of loop quantum gravity effects on Sagittarius A* and M87*

In this study, we delve into the observational implications of rotating Loop Quantum Black Holes (LQBHs) within an astrophysical framework. We employ semi-analytical General Relativistic Radiative Transfer (GRRT) computations to study the emission from the accretion flow around LQBHs. Our findings indicate that the increase of Loop Quantum Gravity (LQG) effects results in an enlargement of the rings from LQBHs, thereby causing a more circular polarization pattern in the shadow images. We make comparisons with the Event Horizon Telescope (EHT) observations of Sgr A* and M87*, which enable us to determine an upper limit for the polymetric function P in LQG. The upper limit for Sgr A* is 0.2, while for M87* it is 0.07. Both black holes exhibit a preference for a relatively high spin (a ≳ 0.5 for Sgr A* and 0.5 ≲ a ≲ 0.7 for M87*). The constraints for Sgr A* are based on black hole spin and ring diameter, whereas for M87*, the constraints are further tightened by the polarimetric pattern. In essence, our simulations provide observational constraints on the effect of LQG in supermassive black holes (SMBH), providing the most consistent comparison with observation.

A Shadow Imaging Bilinear Model and Three-Branch Residual Network for Shadow Removal.

The current shadow removal pipeline relies on the detected shadow masks, which have limitations for penumbras and tiny shadows, and results in an excessively long pipeline. To address these issues, we propose a shadow imaging bilinear model and design a novel three-branch residual (TBR) network for shadow removal. Our bilinear model reveals the single-image shadow removal process and can explain why simply increasing the brightness of shadow areas cannot remove shadows without artifacts. We considerably shorten the shadow removal pipeline by modeling illumination compensation and developing a single-stage shadow removal network without additional detection and refinement networks. Specifically, our network consists of three task branches, i.e., shadow image reconstruction, shadow matte estimation, and shadow removal. To merge these three branches and enhance the shadow removal branch, we design a model-based TBR module. Multiple TBR modules are cascaded to generate an intensive information flow and facilitate feature integration among the three branches. Thus, our network ensures the fidelity of nonshadow areas and restores the light intensity of shadow areas through three-branch collaboration. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods. The model and code are available at https://github.com/nachifur/TBRNet.

Саади и Бунин под тенью Хумая

The article examines the significance of the bird Humay (Khoma) in the culture and mystical literature of Iran and, through them, in Russian literature. The Quran mentions birds associated with the prophets, such as the hoopoe, raven, and quail. In Persian mystical literature, formed on the basis of Islamic culture, symbolic images of light, darkness and shadow occupy a significant place. With their help, poets expressed the spiritual path by which the mystic goes to self-perfection, comprehending the essence of God. The most important work of Iranian mystical literature is “Mantik-ut-tayr” by the poet of the 12th century. Faridaddin Attar, where in an allegorical form a spiritual journey in the name of the search for Truth is described, the result of which is self-knowledge – an important step in the knowledge of God. Saadi Shirazi (13th century) is one of the classics of Iranian literature, whose poems and views greatly influenced a number of Russian writers. It was most noticeable on Ivan Bunin. In his works, Bunin repeatedly mentions the name of Saadi and, imitating him, uses the image of Humay. Saadi Shirazi in his poems presented Humay as a sacred bird, foreshadowing happiness and good. She is a symbol of liberation from the shackles of the material world through spiritual renewal and transcendental transition to the world of the Absolute. “The shadow of the bird Humay, giving good luck and power”, fell on Bunin, who lived 700 years later than Saadi.

A arte da fotografia na China: algumas observações sobre sua conotação estética

Abstract: Photography, as a tool, mainly presents the relationship between people and objects through the use of artistic elements, such as light, shadow and color in images. The creation of photography is an activity that requires inspiration and skills. It requires photographers to carefully and deeply observe and think about the overall structure of the work, in order to take good photos that are both artistic and thoughtful. The development and popularization of photography in today’s society cannot be underestimated for their impact on public aesthetics and humanistic thinking. This paper aims to analyze the aesthetic connotation of Chinese photography and explore its aesthetic standards and values. The philosophical thoughts in Chinese photography are explored, and their reflections and perspectives on life and society are analyzed. The relationship and mutual influence between aesthetic connotations and philosophical thoughts in Chinese photography are explored, and their significance and influence on photography works are analyzed, so as to better understand and appreciate the uniqueness of Chinese photography. Chinese photography is a unique and diverse form of artistic expression that reflects China’s rich cultural heritage and social development. By emphasizing harmony, the integration of painting elements, storytelling and appreciation of nature, Chinese photography provides a profound aesthetic experience and conveys deeper meanings of unity, tranquility, and the relationship between people and the environment.

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Side-scan sonar (SSS) is an essential acoustic sensor device for obtaining underwater information. The instance segmentation of sonar images can effectively locate and detect underwater objects. Although various CNN-based frameworks have achieved promising results in natural image instance segmentation, the noise interference, highlight shadow, and blurred edge in sonar images bring more significant challenges for sonar image instance segmentation. To solve these problems, we propose a novel recurrent pyramid frequency feature fusion network (RPFNet), which mainly consists of the pyramid frequency feature fusion network (PFN), recurrent residual attention mechanism (RRAM), mask prediction module, and semantic segmentation module. By enhancing and fusing different frequency features of SSS images, PFN can effectively extract fine-grained features and reduce background information interference. The RRAM uses residual structure and attention mechanism to enhance the correlation of different frequency features and improve nonlinear feature representation ability. The mask prediction and semantic segmentation modules are used to discriminate object instance categories and generate corresponding semantic segmentation results. We extensively evaluate the proposed framework on the real-scenes sonar image dataset. The evaluation results demonstrate that the proposed method outperforms the state-of-the-art methods in various evaluation metrics. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/darkseid-arch/SonarRPFNet</uri> .

Experimental study on dribbling characteristics of gasoline/biodiesel blends after the end-of-injection

The effects of biodiesel addition on gasoline fuel dribbling process and characteristics after the end-of-injection (EOI) were investigated in a constant volume chamber at different injection pressures and ambient temperatures. The fuel dribbling process was recorded with high spatial and time resolution by using the microscopic shadow imaging technique, and the corresponding dribble velocity, dribble volume and droplet size were extracted. The results showed that the fuel dribbling experienced four typical morphological structures (spray-like, membrane-type, ligament-type, droplets) after the EOI under all the tested conditions. Additionally, increased biodiesel proportion in the blends caused decreased dribble velocity, longer dribbling duration, and increased droplet size, indicating deteriorated breakup and atomization processes of the dribbling flow. Moreover, the higher injection pressure increased the dribble amount but reduced the dribbling duration and droplet size. The increased ambient temperature caused faster evaporation of the dribbling fuel, resulting in a significant decrease in the dribble size. So, these two strategies were believed to be overall favorable to restrain the adverse effect on gasoline fuel dribbling characteristics caused by the addition of biodiesel.

The Design and Development of Traditional Elements in Visual Communication Design under the Perspective of New Media

Abstract This paper takes visual communication design as the entry point and reduces the complexity of the network model and the number of parameters by a convolutional neural network. The Faster R-CNN algorithm has been improved and is now used as the main algorithm for traditional element extraction and detection. The global detection and segmentation extraction algorithm based on U2-Net is utilized to reduce the depth of the network model, and the VGG16 algorithm is improved according to the characteristics of traditional elements, migration learning and its network structure in order to increase the sensory field and solve the problem of loss of accuracy of the feature maps on the basis of not changing the feature layer. An empirical study of traditional element extraction is conducted by using shadow costume patterns as an example. The results show that in multiple shadow images, the range of hues used for the five character types, namely, small Dan, Sheng, Jingsheng, Chou and old Dan, is H ∈ [0°, 210°] ∪ [345°, 360°], and medium-high saturation is dominant, and the range of brightness of the colors used is also basically the same, which is between 0.5-1. This research experiment verifies the feasibility of the element extraction algorithm, which can realize the feature extraction and classification of traditional elements, and its application in visual communication design can improve the visual impact of the works, provide consumers with more diversified design solutions, and can also effectively realize the innovative application and development of traditional elements.

Electrothermal instability in different modes of underwater electrical wire explosion

Electrothermal instability and its effect on the uniformity of discharge channels during underwater electrical wire explosions at different energy deposition rates have been investigated. According to the liquid–vapor transition mechanism, we classified underwater electrical explosion into “phase explosion mode” and “supercritical mode.” Two sets of experiments corresponding to the two modes were conducted, and the exploding wires were observed by a shadow imaging system. Results showed that a bamboo-shaped discharge channel was formed in the phase explosion mode, whereas the discharge channel in the supercritical mode was a uniform cylinder. Self-consistent magnetohydrodynamic simulation results indicated that exploding wires transfer into liquid–vapor coexistence phase in the phase explosion mode, accompanied by a rapid increase in the density perturbation and the formation of bamboo-shaped structures. Moreover, a small perturbation model was derived to analyze the electrothermal instability in different thermodynamic states. It was indicated that the density perturbation is easier to grow in the liquid–vapor coexistence state than the supercritical state, which provides a theoretical explanation for the differences between the two electrical explosion modes.

Hydrodynamics and shape reconstruction of single rising air bubbles in water using high-speed tomographic particle tracking velocimetry and 3D geometric reconstruction

Time-resolved tomographic particle tracking velocimetry (TR-3D-PTV), also called 4D-PTV, is used here to obtain the instantaneous 3D liquid flow field information in the wake of a single rising bubble in water. Simultaneously, the bubble shape, size and velocity are determined by tomographic reconstruction of the 3D bubble shape. Both, tracer particles for PTV and bubbles, are imaged in a shadow mode with background illumination. The Lagrangian method used in this paper, especially combined with the shake the box algorithm, has big advantages compared to particle image velocimetry, in situations, where only low particle per pixel values can be obtained. In this research, single air bubbles of different sizes, with diameters of around 2.4 mm, 4.0 mm, 6.0 mm and 9.6 mm, were injected into stagnant de-ionized water. Their shape was reconstructed in 3D, and an equivalent bubble diameter was determined from this reconstruction. Compared to conventionally used 2D shadow imaging, this diameter is about 13% smaller. The 3D bubble trajectory can be analysed and decomposed into a sinusoidal function curve lateral projection and an ellipsoidal shape vertical projection. As the bubble diameter increases, the radius of the spiral trajectory is decreasing as well as the amplitude of vertical sinusoidal oscillation. The wake structure in the liquid behind the bubbles is also changing with bubble size: from simple vortex pairs for smaller bubbles to an intertwined structure of several twisted vortices for the bigger ones.Graphical abstractThree-dimensional bubble reconstruction (grey surface) and liquid stream lines coloured with velocity magnitude around an ascending air bubble in de-ionized water.

Non-equilibrium plasma assisted ignition characteristics in premixed ethylene-air flow

Reliable and stable ignition under lean conditions is essential for safe operation of the engine. Nanosecond pulsed discharge non-equilibrium plasma assisted ignition characteristics of premixed ethylene-air flow in an advective combustion chamber were investigated. The effects of the equivalence ratio, discharge gap distance, flow velocity, discharge frequency or inter-pulse time, and pulse number were quantified in terms of ignition probability. Shadow images of ignition kernel development were captured and used to extracted the averaged kernel projected area. The results indicated that increasing the equivalence ratio, a higher flow velocity, a wider discharge gap distance, and a larger number of pulses are all conducive to the increasing of ignition probability via inducing a larger initial kernel. Increasing inter-pulse time has a non-monotonic effect on ignition probability for multiple nanosecond pulsed discharges ignition. As the inter-pulse time decreases, when neighboring kernel boundaries happen to overlap each other, the partially-coupled regime shows a higher ignition probability. Longer or shorter inter-pulse time both cause the decrease in ignition probability. The shortest inter-pulse time shown as the fully-coupled regime is the most favorable for ignition with the highest ignition probability. A method is proposed to estimate the critical frequency at which partially-coupled regime transitions to fully-coupled regime by 95% of the asymptotic time of flame development time.

A Multi-Functional CMOS Biosensor Array With On-Chip DEP-Assisted Sensing for Rapid Low-Concentration Analyte Detection and Close-Loop Particle Manipulation With No External Electrodes.

This article presents a fully-integrated dielectrophoresis (DEP)-assisted multi-functional CMOS biosensor array chip with 4096 working electrodes (WEs), 12288 photodiodes (PDs), reference electrodes (REs), and counter electrodes (CEs), while each WE and photodiode can be reconfigured to support on-chip DEP actuation, electrochemical potentiostat, optical shadow imaging, and complex impedance sensing. The proposed CMOS biosensor is an example of an actuation-assisted label-free biosensor for the rapid sensing of low-concentration analytes. The DEP actuator of the proposed CMOS biosensor does not require any external electrode. Instead, on-chip WE pairs can be re-used for DEP actuation to simplify the sensor array design. The CMOS biosensor is implemented in a standard 130-nm BiCMOS process. Theoretical analyses and finite element method (FEM) simulations of the on-chip DEP operations are conducted as proof of concept. Biological assay measurements (DEP actuation/electrochemical potentiostat/impedance sensing) with E.coli bacteria and microbeads (optical shadow imaging) demonstrate rapid detection of low-concentration analytes and simultaneous manipulation and detection of large particles. The on-chip DEP operations draw the analytes closer to the sensor electrode surface, which overcomes the diffusion limit and accelerates low-concentration analyte sensing. Moreover, the DEP-based movement of large particles can be readily detected by on-chip photodiode arrays to achieve close-loop manipulation and sensing of particles and droplets. These show the unique advantages of the DEP-assisted multi-functional biosensor.