Shadow Images Research Articles

Enhanced osmosis model with bilateral total variation for effective shadow removal

This research paper presents an innovative technique for shadow images removal. The method involves redefining a contemporary osmosis model by incorporating bilateral total variation (TV) operators. This integration allows to take advantage of robust anisotropic diffusion, resulting in improved image restoration. The paper also outlines new mathematical derivation of the bilateral TV and a combination with nonlinear anisotropic transport term. The experimental results substantiate the effectiveness of the anisotropic osmosis model, showcasing its superior qualitative and quantitative performance when compared to current state-of-the-art techniques.

Rockery morphology based on quantitative analysis of shading

The rockeries of classical Chinese gardens are masterpieces of classical Chinese garden art and form a key element of garden heritage. Consequently, a quantitative study of “rockery shadows” is of considerable importance to the study of “rockery forms.” In this study, we selected the representative North Rockery in Zhanyuan Garden of Nanjing and Ruiyunfeng, Guanyunfeng, and Yulinglong as the objects of the study. This study aims to explore the shadow images of rockeries using image extraction and targeted quantitative analysis methods. Macroscopically, the overall shape of rockery shadows was described using fractal dimensions; microscopically, the aspect ratio, angle, and refinement indexes of each shadow based on each observation angle of the rockery were measured using the PAT-GEOM plug-in in ImageJ software. SPSS Statistics was used for the normal distribution test of the angular distribution data. Consequently, the shadow data of the North Rockery in Zhanyuan Garden and Ruiyunfeng, Guanyunfeng, and Yulinglong, respectively, were analyzed and compared, and four rockery-shadow laws were derived. Finally, the results were applied to the design of the rockery morphological translation based on quantitative analysis of the shadows. The approach presented here will enhance landscape design, support environmental planning, and preserve cultural heritage.

Research on 3D Model Calculation of Green and low-carbon New Buildings

Due to the widespread use of solid clay bricks in building houses in China, the annual destruction of farmland by burning bricks amounts to about 100000 acres. Faced with these problems, China has an extremely urgent demand for green and low-carbon buildings. Therefore, this article conducts computational research on the 3D model of a new type of green and low-carbon building. Due to the significant differences in the geometric form and texture representation of a large number of green and low-carbonl new buildings, it is difficult to uniformly quantify their level of detail. This article considers the usefulness and accessibility of 3D model calculation data separately. The shadow length is generally the distance from the selected building's facade roof to the end of the shadow, which can avoid the influence of building shape on its length. The determination of these two points and the calculation of the distance between them will directly affect the accuracy of shadow length calculation. Therefore, when users need to add an application subsystem to the system, they must add information such as the input and output data structures of the subsystem to the module. The height information calculation method uses the building and shadow imaging geometric modeling to calculate the building height by establishing the relationship between the shadow length and the corresponding building height.

Quantum-gravitational effects on Joule-Thomson expansion and black hole shadows in F(R) gravity with barrow entropy corrections

We present a novel investigation into the thermodynamic properties and shadow images of a topological phantom AdS black hole within the framework of F(R) gravity, utilizing the Barrow entropy formulation. We introduce a detailed study of the Joule-Thomson expansion, calculating the Joule-Thomson coefficient and mapping isenthalpic and inversion curves to characterize heating/cooling phases. Our findings reveal that the Barrow parameter plays a crucial role in altering the shape and position of these thermodynamic curves, thereby significantly impacting the black hole's thermal behavior. Moreover, we demonstrate that variations in F(R) gravity parameters such as the scalar curvature R0, the type of field interaction η, and fR0—lead to substantial modifications in the shadow of the black hole. These changes in shadow size and shape underscore the direct influence of the underlying gravitational theory on the interaction between black holes and light, offering new perspectives on how black holes are perceived by distant observers. Additionally, we calculate the total observed intensities from the emission function of the accretion disk, with graphical analysis showing that the F(R) gravity parameters markedly affect the intensity distribution around the black hole. This has significant implications for the accretion disk's emission properties and the resulting shadow image, highlighting the critical impact of modified gravity theories on observable black hole phenomena. This work offers fresh insights and underscores the importance of considering modified gravity frameworks when studying black hole thermodynamics and optical properties.

Image shadow removal based on pseudo-illuminated region and log domain transformation

ABSTRACT The presence of shadows in images can significantly degrade image quality. To address the challenges, this study utilizes pseudo-illumination regions and logarithmic domain transformations. Firstly, the input shadow image and its corresponding shadow region are segmented and clustered into multiple image blocks. Then, a comprehensive consideration of distance, color, and texture features is employed to construct pseudo illumination regions. Subsequently, the designed logarithmic domain transformation function is utilized for shadow removal. Finally, color correction and optimization of shadow edges complete the overall process. The feasibility of this approach has been preliminarily verified through experimental analysis.

Frequency‐Aware Facial Image Shadow Removal through Skin Color and Texture Learning

AbstractExisting facial image shadow removal methods predominantly rely on pre‐extracted facial features. However, these methods often fail to capitalize on the full potential of these features, resorting to simplified utilization. Furthermore, they tend to overlook the importance of low‐frequency information during the extraction of prior features, which can be easily compromised by noises. In our work, we propose a frequency‐aware shadow removal network (FSRNet) for facial image shadow removal, which utilizes the skin color and texture information in the face to help recover illumination in shadow regions. Our FSRNet uses a frequency‐domain image decomposition network to extract the low‐frequency skin color map and high‐frequency texture map from the face images, and applies a color‐texture guided shadow removal network to produce final shadow removal result. Concretely, the designed fourier sparse attention block (FSABlock) can transform images from the spatial domain to the frequency domain and help the network focus on the key information. We also introduce a skin color fusion module (CFModule) and a texture fusion module (TFModule) to enhance the understanding and utilization of color and texture features, promoting high‐quality result without color distortion and detail blurring. Extensive experiments demonstrate the superiority of the proposed method. The code is available at https://github.com/laoxie521/FSRNet.

Automated Cloud Shadow Detection from Satellite Orthoimages with Uncorrected Cloud Relief Displacements

Clouds and their shadows significantly affect satellite imagery, resulting in a loss of radiometric information in the shadowed areas. This loss reduces the accuracy of land cover classification and object detection. Among various cloud shadow detection methods, the geometric-based method relies on the geometry of the sun and sensor to provide consistent results across diverse environments, ensuring better interpretability and reliability. It is well known that the direction of shadows in raw satellite images depends on the sun’s illumination and sensor viewing direction. Orthoimages are typically corrected for relief displacements caused by oblique sensor viewing, aligning the shadow direction with the sun. However, previous studies lacked an explicit experimental verification of this alignment, particularly for cloud shadows. We observed that this implication may not be realized for cloud shadows, primarily due to the unknown height of clouds. To verify this, we used Rapideye orthoimages acquired in various viewing azimuth and zenith angles and conducted experiments under two different cases: the first where the cloud shadow direction was estimated based only on the sun’s illumination, and the second where both the sun’s illumination and the sensor’s viewing direction were considered. Building on this, we propose an automated approach for cloud shadow detection. Our experiments demonstrated that the second case, which incorporates the sensor’s geometry, calculates a more accurate cloud shadow direction compared to the true angle. Although the angles in nadir images were similar, the second case in high-oblique images showed a difference of less than 4.0° from the true angle, whereas the first case exhibited a much larger difference, up to 21.3°. The accuracy results revealed that shadow detection using the angle from the second case improved the average F1 score by 0.17 and increased the average detection rate by 7.7% compared to the first case. This result confirms that, even if the relief displacement of clouds is not corrected in the orthoimages, the proposed method allows for more accurate cloud shadow detection. Our main contributions are in providing quantitative evidence through experiments for the application of sensor geometry and establishing a solid foundation for handling complex scenarios. This approach has the potential to extend to the detection of shadows in high-resolution satellite imagery or UAV images, as well as objects like high-rise buildings. Future research will focus on this.

Efficient Environment Map Rendering Based on Decomposition

AbstractThis paper presents an efficient environment map sampling algorithm designed to render high‐quality, low‐noise images with only a few light samples, making it ideal for real‐time applications. We observe that bright pixels in the environment map produce high‐frequency shading effects, such as sharp shadows and shading, while the rest influence the overall tone of the scene. Building on this insight, our approach differs from existing techniques by categorizing the pixels in an environment map into emissive and non‐emissive regions and developing specialized algorithms tailored to the distinct properties of each region. By decomposing the environment lighting, we ensure that light sources are deposited on bright pixels, leading to more accurate shadows and specular highlights. Additionally, this strategy allows us to exploit the smoothness in the low‐frequency component by rendering a smaller image with more lights, thereby enhancing shading accuracy. Extensive experiments demonstrate that our method significantly reduces shadow artefacts and image noise compared to previous techniques, while also achieving lower numerical errors across a range of illumination types, particularly under limited sample conditions.

Study in the Extreme UV Range of the Spectral Transmission of Nickel Plasma Created under the Effect of an X-Ray Pulse of the Z-Pinch

Spectral properties of the high-temperature plasma obtained by exposing a nickel layer to a source of high-power X-ray radiation (a power of 6–10 TW with a duration of 7–10 ns) based on a Z-pinch, formed during implosion of tungsten multi-wire arrays at the Angara-5-1 facility, are studied. In this case, the Z-pinch radiation heats the target and turns it into the hot plasma, and the same radiation probes the target plasma to determine the spectral dependence of the transmission of this plasma. An original scheme is proposed for measuring the incident, transmitted and self-emission of a target simultaneously in one experiment in the frame mode using a grazing incidence diffraction spectrograph. Using laser shadow imaging, experimental data are obtained on the velocity of the plasma expansion on the irradiated and back sides of the target, which reached 100 km/s. Targets made of thin Ni layers deposited on a mylar film are studied. An irradiationinduced multiple increase in the transmission of the target plasma in the EUV range is observed compared to the transmittance of the target in the cold state. The dependence of the absorption spectrum of the plasma and the accompanying self-radiation of the target on the power and shape of the heating pulse is studied. The measurement results are compared with numerical calculations performed using the RALEF-2D two-dimensional radiation code, which has been repeatedly used previously to simulate similar experiments. The shape of the spectral dependence of the transmission in the experiment and calculation is similar in the range of ∼30–200 Å, but the model plasma transmission (∼0.8–0.9) is higher than that obtained using a spectrograph and X-ray multi-frame photography (∼0.5–0.6).

Two-stage deep image restoration network with application to single image shadow removal

In this paper, we introduce a two-stage deep learning-based image restoration network and its application to remove shadow information from a single image, named by ESCNet (Encoder-decoder based Shadow removal with Colorization Network). Most existed single image-based shadow removal methods may suffer from that the shadow contains multiple regions of different colors or rich image details. To tackle with the problems, our key idea is to first remove shadow(s) from an image followed by repainting the shadow-removed region(s) in this image. To accomplish this, we present a deep two-stage network, cascading a shadow removal network (SRN) and a colorization network (CN). The presented encoder-decoder-based SRN with fusion of global and local feature information is used to remove the shadow(s) in the grayscale domain of the input image while recovering the image details for the shadow-removed region(s). Then the proposed CN aims at repainting the removed shadow region(s) via re-colorization. The proposed deep model has been well trained and well evaluated on the two well-known public datasets, i.e., ISTD (Image Shadow Triplets Dataset) and SRD (Shadow Removal Dataset). Experimental results have shown that the proposed method outperforms the compared state-of-the-art (SOTA) shadow removal approaches quantitatively and qualitatively.

SSM-CloudNet: state space model-based cloud and cloud shadow detection network for Sentinel-2 imagery

Abstract Remote sensing images acquired by satellite sensors are inevitably obscured by clouds and cloud shadows, which obscure true ground reflectance information in those areas. Therefore, to ensure the accuracy and reliability of downstream analyses, detecting clouds and their shadows in satellite images is crucial. This paper proposes a state space model-based cloud and cloud shadow detection network for Sentinel-2 imagery, named SSM-CloudNet. This is the first time that a state space model (SSM) is applied to cloud and cloud shadow detection tasks, combining the efficient feature extraction capability of the symmetric encoder-decoder structure with the accuracy of SSM in cloud feature extraction, and providing an innovative approach to this task. Experiments demonstrate that SSM-CloudNet performs exceptionally well across multiple metrics, proving the high effectiveness of the proposed method.

Gas flows generated by pulse-periodic optical breakdown and quiet optical discharge

Quiet optical discharge in high-pressure xenon is visually stable pre-breakdown stage of optical discharge supported by pulse-periodic short-wavelength infrared laser radiation with intensity of the order of 109 W/cm2. Increasing the laser intensity leads to the transition of the quiet discharge into a pulse-periodic optical breakdown. In this study, quasi-stationary directional flows generated by both the quiet discharge and the optical breakdown are observed. The flows and their initial stages under limited number of discharge pulses are visualized by shadow imaging method using a point-like laser-plasma radiation source. It is shown that the gas streams outflow points in a quiet discharge correspond to the positions of the laser radiation intensity local maxima in the focal beam waist with astigmatism complicated by defocusing effect of thermal lens arising in the discharge zone. The pulse-periodic optical breakdown emits a turbulent gas flow toward the laser beam. The beam refraction on density gradients in the turbulent flow causes the breakdown instability from pulse to pulse. It was found that time-average absorption of laser radiation in a quiet discharge is about 3% (also in a pulse), while that in optical breakdown is 15% and higher (from 70% to 100% in a pulse). Laser beam intensity for quiet discharge at pulse repetition rate νp = 20 kHz ranges from 1.3 × 109 to 1.5 × 109 W/cm2. At intensities above 4 × 109 W/cm2 and repetition rates νp > 50–55 kHz, laser breakdowns are observed in each laser pulse with the focal ratio f/d ≤ 7. At f/d = 10.6 and νp > 28 kHz, the quiet discharge does not transit to laser breakdown due to defocusing by the thermal lens induced.

CRFormer: A cross-region transformer for shadow removal

Image shadow removal is a fundamental task in computer vision, which aims to restore damaged signals caused by shadows, thereby improving image quality and scene understanding. Recently, transformers have demonstrated strong capabilities in various applications by capturing global pixel interactions, a capability highly desirable for shadow removal. However, applying transformers to promote shadow removal is non-trivial for the following two reasons: 1) The patchify operation is not suitable for shadow removal due to irregular shadow shapes; 2) Shadow removal only requires one-way interaction from the non-shadow region to the shadow region instead of the common two-way interactions among all pixels in the image. In this paper, we propose a novel Cross-Region transFormer (CRFormer) for shadow removal which differs from existing transformers by only considering the pixel interactions from the non-shadow region to the shadow region without splitting images into patches. This is achieved by a carefully designed region-aware cross-attention mechanism that aggregates the recovered shadow region features, conditioned on the non-shadow region features. Extensive experiments on the ISTD, AISTD, SRD, and Video Shadow Removal datasets demonstrate the superiority of our method compared to other state-of-the-art methods.

An improved EnlightenGAN shadow removal framework for images of cracked concrete

The concrete crack images in engineering are usually obscured by unexpected shadow and their removal is usually required which is always a challenging task due to its complexity and diversity. An improved Enlighten Generative Adversarial Networks (IEnlightenGAN) is developed in a proposed framework to improve shadow removal results. The Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) is firstly developed to improve the concrete image resolution. Residual-in-residual-dense blocks module and Atrous Spatial Pyramid Pooling module are then integrated into the IEnlightenGAN to improve its shadow removal capability. A quality assessment methodology on the shadow removal results is also proposed. The crack contour is extracted based on U-Net, and denoising is conducted based on morphological closing. Image segmentation metrics are used for the evaluation. The proposed IEnlightenGAN and the evaluation method are trained and verified using a dataset containing both normal and shadowed concrete crack images with comparison of results from the state-of-the-art models. The proposed shadow removal framework is found able to improve the quality of shadowed crack images, and the proposed quality assessment method is also found better than existing approach.

Real-Space Tilting Method for Atomic Resolution STEM Imaging of Nanocrystalline Materials.

Atomic-resolution scanning transmission electron microscopy (STEM) characterization requires precise tilting of the specimen to a high symmetric zone axis, which is usually processed in reciprocal space by following the diffraction patterns. However, for small-sized nanocrystalline materials, their diffraction patterns are often too faint to guide the tilting process. Here, a simple and effective tilting method is developed based on the diffraction contrast change of the shadow image in the Ronchigram. The misorientation angle of the specimen can be calculated and tilted to the zone axis based on the position of the shadow image with lowest intensity. This method requires no prior knowledge of the sample and the maximum misorientation angle that can be corrected is >±6.9° with sub-mrad accuracy. It operates in real space, without recording the diffraction patterns of the specimens, making it particularly effective for nanocrystalline materials. Combined with the scripting to control the microscope, the sample can be automatically tilted to the zone axis under low dose conditions (<0.17 e-Å- 2s-1), facilitating the imaging of beam sensitive materials such as zeolites or metal-organic frameworks. This automated tilting method can significantly contribute to the atomic-scale characterization of the nanocrystalline materials by STEM imaging.

Experimental measurement of the size distribution of microbubbles prepared by reciprocating syringe technique

Microbubbles have captured the attention of scholars due to their exceptional physical and chemical properties that exhibit practical applications. Transcranial Doppler ultrasound is a diagnostic tool used in clinical medicine to identify patent foramen ovale (PFO) heart disease caused by incomplete fusion of primary and secondary septum after birth, using microbubbles. The volume of gas and size of the bubbles determine the consequences of air entering the bloodstream. Therefore, it is imperative to investigate stable and efficient microbubble generation technology. The team designed and developed a new type of bubble generator and performed basic research on the flow and mixing rules in the gas-liquid two-phase system in the syringe. The shadow imaging method was used to explore the influence of the equipment operation parameters on the experimental system's microbubble flow process. After analyzing multiple sets of experimental data, it was found that the diameter of bubbles decreases as the frequency increases. After ten times of high-frequency reciprocating movement of the equipment, the microbubble size distribution is mainly between 10–100 μm, the mean diameter is between 23–26 μm, and the relative standard deviation is less than 4.5 %. Compared to manual methods, the device exhibits good accuracy and repeatability.

Secret image sharing with distinct covers based on improved Cycling-XOR

Secret image sharing (SIS) is a technique used to distribute confidential data by dividing it into multiple image shadows. Most of the existing approaches or algorithms protect confidential data by encryption with secret keys. This paper proposes a novel SIS scheme without using any secret key. The secret images are first quantized and encrypted by self-encryption into noisy ones. Then, the encrypted images are mixed into secret shares by cross-encryption. The image shadows are generated by replacing the lower bit-planes of the cover images with the secret shares. In the extraction phase, the receiver can restore the quantized secret images by combinatorial operations of the extracted secret shares. Experimental results show that our method is able to deliver a large amount of data payload with a satisfactory cover image quality. Besides, the computational load is very low since the whole scheme is mostly based on cycling-XOR operations.

Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R2 of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R2 of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels.

Microwave-induced plasma on spherical flame dynamics of spark-ignited hydrogen-air under water dilution

Microwave-assisted spark ignition (MAI) offers a commercially feasible way to enhance conventional spark ignition performance under extreme conditions. While previous studies suggested microwave pulses had no obvious effect on self-sustained flame from the perspective of energy deposition, the hydrodynamic effect of microwave plasma remained under-explored. This study experimentally investigated the effect of the microwave-induced plasma on lean hydrogen flame dynamics (Lewis number ∼ 0.35), examining the influence of water content (rH2O) in this process under different pulse repetition frequency (PRF) and pressures. Using a linear high-speed shadow imaging system coupled with electrical diagnostics, we synchronously recorded the flame and plasma morphology evolution while monitoring spark and microwave energy. Results revealed that microwave pulses during spark ignition generated wrinkles and perturbances accelerating the cracking and cellularization of the subsequent self-sustained flame. This effect intensified with increasing PRF from 1 to 10 kHz, particularly at high rH2O. The overall microwave impact on early flame development was more pronounced at high rH2O, attributed to stronger interactions between the microwave plasma jet and flame front due to reduced distance. Interestingly, electrical diagnostics showed an inversed relationship between total absorbed microwave energy and rH2O, highlighting the crucial role of the first microwave pulse at 1 kHz PRF. This study demonstrates that early microwave pulses can influence hydrogen flame dynamics even in the self-sustained stage, offering new insights into MAI mechanisms. These findings open avenues for research into active control of self-sustained flame dynamics, potentially leading to improved ignition strategies in challenging combustion environments.

Study on the microscopic characteristics of pentanol/highly active fuel spray based on high-speed droplet tracking velocimetry technology

Pentanol blending with highly reactive fuels for internal combustion engines holds considerable promise. The atomization of sprays closely influences the efficiency and combustion characteristics of the system. Based on Particle Tracking Velocimetry and high-speed shadow imaging technology, we propose a method termed High-speed Droplet Tracking Velocimetry for analyzing spray particle size. Prior to conducting our study, we validated the reliability of High-speed Droplet Tracking Velocimetry through comparisons with the Malvern and Phase Doppler methods. This study utilized long working distance micro high-speed shadow imaging technology in a visualized constant volume combustion chamber to investigate the spray micro-characteristics of pentanol blended with highly reactive fuels, including Fatty Acid Methyl Ester, Hydrogenated Catalytic Biodiesel, and Diesel. The results indicate that under all operating conditions, the droplets of P20H80 (the volume fraction of 20 % n-pentanol mixed with 80 % Hydrogenated Catalytic Biodiesel) are smaller and more uniformly distributed, demonstrating the minimum Sauter Mean Diameter, characteristic diameter, and width of droplet size distribution. The droplet velocities of P20H80 are the lowest, followed by P20Di80 (the volume fraction of 20 % n-pentanol mixed with 80 % Diesel), and P20B80 (the volume fraction of 20 % n-pentanol mixed with 80 % Fatty Acid Methyl Ester) exhibits the highest velocities. These velocity differences among the three fuels generally remain within 10 %-20 % across various injection pressures. P20Di80 shows the highest Reynolds and Weber numbers, followed by P20H80, with P20B80 having the smallest values due to its intermediate physical properties. The gaps between each fuel type range from 20 % to 30 %.