Image Formation Process Research Articles

Revisiting a destination image model in the social media context

ABSTRACT Given the growing importance of social media (SM) for tourism promotions, the present study examined a destination image model that includes SM as image formation agents (IFAs) (autonomous, induced, and organic), cognitive image (attractions and support), affective image, and social distance as a moderator, grounded in construal level theory. With the sample of tourists (N = 699), structural equation modeling revealed that each IFA group had different patterns of effects in shaping cognitive image, which in turn influenced affective image. Latent moderated structural equation modeling analysis showed that social distance had a negative interacting effect with each of the IFA groups in forming the attractions image, highlighting the significant moderating role of social distance in the process. This study enriches an understanding on the crucial roles of IFAs and social distance in the image formation process within the SM context.

Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise

We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson and Gaussian noise. The spatial variation of the point spread function of a light-sheet microscope is determined by the interaction between the excitation sheet and the detection objective PSF. We introduce a model of the image formation process that incorporates this interaction and we formulate a variational model that accounts for the combination of Poisson and Gaussian noise through a data fidelity term consisting of the infimal convolution of the single noise fidelities, first introduced in L. Calatroni et al. (SIAM J Imaging Sci 10(3):1196–1233, 2017). We establish convergence rates and a discrepancy principle for the infimal convolution fidelity and the inverse problem is solved by applying the primal–dual hybrid gradient (PDHG) algorithm in a novel way. Numerical experiments performed on simulated and real data show superior reconstruction results in comparison with other methods.

Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy.

Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target images, our method greatly reduces the effort to be put into practice as the training of a network requires only a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport-driven cycle-consistent generative adversarial network that learns from an unpaired matching between high-resolution 2D images in the lateral image plane and low-resolution 2D images in other planes. Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution but also restores suppressed visual details between the imaging planes and removes imaging artifacts.

Single Image Haze Removal Based on a Simple Additive Model With Haze Smoothness Prior

Single image haze removal, which is to recover the clear version of a hazy image, is a challenging task in computer vision. In this paper, an additive haze model is proposed to approximate the hazy image formation process. In contrast with the traditional optical model, it regards the haze as an additive layer to a clean image. The model thus avoids estimating the medium transmission rate and the global atmospherical light. In addition, based on a critical observation that haze changes gradually and smoothly across the image, a haze smoothness prior is proposed to constrain this model. This prior assumes that the haze layer is much smoother than the clear image. Benefiting from this prior, we can directly separate the clean image from a single hazy image. Experimental results and comparisons with synthetic images and real-world images demonstrate that the proposed method outperforms state-of-the-art single image haze removal algorithms.

Tomographic single pixel spatial frequency projection imaging

Conventional methods for three-dimensional (3D) imaging frequently rely on voxel-by-voxel data acquisition, which restricts the range of specimens in which they can be effectively employed. While advances in imaging technology now permit the routine acquisition of 3D images approaching video rates, there are other limitations to image formation in fluorescent microscopy that prohibit studies in large volume samples, highly scattering media, and dynamic environments. Some approaches to 3D image collection circumvent this need by the use of tomographic imaging, where sub-3D projections are collected at varying illumination angles and reconstructed through an inversion algorithm to compute an estimate of the 3D fluorophore distribution. Many such methods rely on spatially coherent light, and thus prohibit the use of fluorescent light. By employing unique spatio-temporally varying illumination patterns in conjunction with computational imaging approaches to image reconstruction, we show that some limitations of laser scanning and wide-field imaging can be overcome. We outline several approaches that utilize tomographic projections with patterned illumination to collect 3D image data. All three dimensional optical imaging exploits projection of the desired 3D information into a lower-dimensional subspace, and then a full three dimensional object is estimated from these data. We discuss a number of such single pixel strategies that project object information onto a zero-dimensional, usually a power, measurement. Further, we outline computational image reconstruction approaches that enhance the object estimates by employing a forward model for the image formation process.

Synthetic Aperture Radar Active Decoy

Synthetic aperture radar (SAR) has been widely involved in military reconnaissance and detection of moving targets. SAR active decoy can generate deceptive jamming signal and it can severely affect the content of SAR focused image. SAR range Doppler algorithm (RDA) is considered as one of the most common algorithms utilized in the image formation processor (IFP) in SAR sensors. In this paper, the inverse RDA (IRDA) deception jamming technique and its evaluation criteria on SAR are demonstrated. Mathematical formulations for SAR RDA with and without deception jamming are presented. Matlab simulation and results of SAR RDA under the proposed deception scenario are discussed. SAR point target simulation of the proposed deceptive jamming and false target insertion into real large scene are also introduced. Comparison of the proposed deceptive jamming to signal power ratio (JSR) required to counter SAR with other SAR jamming techniques is analyzed.

Modeling Several Optical Components Using Scalar Diffraction Theory

Compound systems are generally treated by geometrical optics, for instance, through the Gauss’ formalism. The objective is to simplify the process of image formation. However, this formalism does not include the wave characteristics of light and boundary effects. The treatment of diffraction is not straightforward. Thus, the extension of this formalism towards the scalar theory of diffraction is very desired. This work offers this extension and emphasizes its importance. Compound systems, including the human eye, are then modeled by Fresnel theory. For illustration, a lens-based model of the Fresnel transform is used to treat the human eye system.

Особенности негативного информационно-психологического воздействия и его влияние на осуществление профессиональной деятельности сотрудников отделов информации и общественных связей органов внутренних дел Российской Федерации.

Abstract: the article analyzes the role of information posted on the Internet, the ability to form competencies demanded by modern society, the influence of personal qualities that contribute to reducing the level of negative information and psychological impact.
 Employees of the information and Public Relations departments of the Ministry of Internal Affairs of Russia interact with representatives of various mass media, monitor social networks and have access to information that affects both positively and negatively on a police officer. The confirmation is the analysis of the number of media outlets that publish materials about the activities of internal affairs bodies. when evaluating the effectiveness of professional communication, it is always necessary to take into account the nature of employee relationships, which largely depend on their personal characteristics and communication culture.
 In order to minimize the negative information and psychological influence in the internal affairs bodies, tasks are being implemented aimed at improving the effectiveness of information and communication, affective and communicative, and regulatory and communicative aspects of interaction. In interpersonal communication, the multilevel process of image formation is carried out on the basis of a "subjective reading" of physical characteristics and psychological properties based on behavior, which makes it possible to prevent the occurrence of negative psychological influence. The article analyzes the increasing role of the Internet, presented in comparison for the period from 2001 to 2021. An empirical study has been conducted to identify competencies that contribute to minimizing the negative impact of destructive information on police officers, to systematize the forms of organizing information support for the activities of internal affairs bodies that contribute to overcoming the negative information and communication impact. Using the methods of questioning a group of employees responsible for interaction with the media of the Ministry of Internal Affairs of Russia in the Chelyabinsk region, the competencies that make up a set of professional qualities and communicative personality traits that contribute to increasing the level of psychological readiness for the negative impact of information were determined.

Underwater Image Enhancement Based on Histogram-Equalization Approximation Using Physics-Based Dichromatic Modeling.

This work proposes to develop an underwater image enhancement method based on histogram-equalization (HE) approximation using physics-based dichromatic modeling (PDM). Images captured underwater usually suffer from low contrast and color distortions due to light scattering and attenuation. The PDM describes the image formation process, which can be used to restore nature-degraded images, such as underwater images. However, it does not assure that the restored images have good contrast. Thus, we propose approximating the conventional HE based on the PDM to recover the color distortions of underwater images and enhance their contrast through convex optimization. Experimental results demonstrate the proposed method performs favorably against state-of-the-art underwater image restoration approaches.

Self-Supervised Deep Learning for Improved Image-Based Wave-Front Sensing

Phase retrieval from supervised learning neural networks is restricted due to the problem of obtaining labels. To address this situation, in the present paper, we propose a phase retrieval model of self-supervised physical deep learning combined with a complete physical model to represent the image-formation process. The model includes two parts: one is MobileNet V1, which is used to map the input samples to the Zernike coefficients, the other one is an optical imaging system and it is used to obtain the point spread function for training the model. In addition, the loss function is calculated based on the similarity between the input and the output to realize self-supervised learning. The root-mean-square (RMS) of the wave-front error (WFE) between the input and reconstruction is 0.1274 waves in the situation of D/r0 = 20 in the simulation. By comparison, The RMS of WFE is 0.1069 waves when using the label to train the model. This method retrieves numerous wave-front errors in real time in the presence of simulated detector noise without relying on label values. Moreover, this method is more suitable for practical applications and is more robust than supervised learning. We believe that this technology has great applications in free-space optical communication.

Thailand’s Image from the Perspectives of Chinese Non-Visitors and Visitors

This study examined Chinese non-visitors’ and visitors’ perceptions of Thailand as an outbound destination. It employed a qualitative approach using semi-structured interviews. Data were gathered from 24 Chinese respondents, of which 12 had visited Thailand and 12 had not. Thematic analysis was undertaken. The findings showed that the perceptions of non-visitors and visitors were quite different, mainly due to the types of information sources these people used in their destination image formation process. The non-visitors tended to have positive images, whereas the visitors had more mixed perceptions of Thailand.

Investigation of a spectral lens for the formation of a normalized difference vegetation index NDVI0.705

Subject of study. A new type of diffractive optical element separating radiation at the wavelengths of 705 and 750 nm into the ±1 diffraction orders is investigated as a basis for a sensor for the detection of the normalized difference vegetation index NDVI0.705. Method. The study was performed as a natural experiment using a white light source with a continuous spectrum. Main results. A diffractive optical element separating radiation at the wavelengths of 705 and 750 nm into the ±1 diffraction orders was investigated. Using this optical element allows index images to be obtained directly instead of through the complex acquisition of a full hyperspectral image for the detection of NDVI0.705 in the region of a near-infrared slope. The diffractive optical element was fabricated using direct laser writing in a photoresist. The height of the formed microrelief was 4 µm, which implies that a combination of diffractive and refractive properties of this element contribute to the image formation process. The suggested term for these diffractive optical elements is a spectral diffractive lens. A laboratory setup was assembled based on the fabricated spectral diffractive lens to confirm the possibility of operating this lens in the imaging mode. In first experiments, a high-power halogen lamp behind an opaque screen with holes with diameters of 0.4–0.5 µm (individual and in linearly positioned groups) was used to simulate small-scale radiating objects. Two diffraction orders were recorded on a light-sensitive matrix, namely, −1 for radiation with a wavelength of 705 nm and +1 for radiation with a wavelength of 750 nm. The similarity of the geometrical parameters of the images of the group of sources in the +1 and −1 orders confirmed the possibility of using this spectral diffractive lens in the imaging mode. Therefore, a slit aperture was mounted instead of the linearly positioned sources in the next experiment. Scanning of a section of a color table with subsequent assembly of an index image was performed through this aperture. Practical significance. The spectral diffractive lens proposed in this work can be used to create simple compact sensors for the real-time monitoring of vegetation cover and for specialized agricultural equipment.

Three-dimensional microscopic single-pixel imaging with chaotic light

Three-dimensional (3D) microscopic imaging is fundamentally important for microscale observations and manipulations. Here, we show that single-pixel imaging (SPI) is available for realizing superior 3D microscopic imaging, which relies on the near-field illumination with chaotic light. Like considering a two-point object to investigate two-dimensional microscopic imaging, we theoretically and experimentally studied the image formation process for a 3D object with two slices, demonstrating the excellent capability of the SPI for 3D microscopic imaging. The proposed 3D microscopic imaging via SPI would be useful for applications such as biological imaging and micromanipulations.

A Survey on Intrinsic Images: Delving Deep into Lambert and Beyond

Intrinsic imaging or intrinsic image decomposition has traditionally been described as the problem of decomposing an image into two layers: a reflectance, the albedo invariant color of the material; and a shading, produced by the interaction between light and geometry. Deep learning techniques have been broadly applied in recent years to increase the accuracy of those separations. In this survey, we overview those results in context of well-known intrinsic image data sets and relevant metrics used in the literature, discussing their suitability to predict a desirable intrinsic image decomposition. Although the Lambertian assumption is still a foundational basis for many methods, we show that there is increasing awareness on the potential of more sophisticated physically-principled components of the image formation process, that is, optically accurate material models and geometry, and more complete inverse light transport estimations. We classify these methods in terms of the type of decomposition, considering the priors and models used, as well as the learning architecture and methodology driving the decomposition process. We also provide insights about future directions for research, given the recent advances in neural, inverse and differentiable rendering techniques.

Computational Imaging XX Conference Overview and Papers Program

More than ever before, computers and computation are critical to the image formation process. Across diverse applications and fields, remarkably similar imaging problems appear, requiring sophisticated mathematical, statistical, and algorithmic tools. This conference focuses on imaging as a marriage of computation with physical devices. It emphasizes the interplay between mathematical theory, physical models, and computational algorithms that enable effective current and future imaging systems. Contributions to the conference are solicited on topics ranging from fundamental theoretical advances to detailed system-level implementations and case studies.

Radiometric Degradation Associated With Terrain Height Variations and Pulse Duration in Scan-On-Receive SAR Images

Scan-on-receive (SCORE) is a key digital beamforming (DBF) technique for future high-resolution wide-swath spaceborne synthetic aperture radar (SAR) systems. Compared to a conventional approach, it allows improving the signal-to-noise ratio and the range ambiguity suppression. Nevertheless, it also exposes the system to new errors, associated with terrain height variations and pulse duration. This work investigates the mutual effect of these error sources on the SCORE SAR image. A novel, closed, a mathematical expression is derived, respectively, for the impulse response function of the image formation process and for the radiometric loss affecting the image pixels. This makes it possible to predict and quantify the effect of terrain height variations and pulse duration as a function of the system, processing, and geometric parameters. The numerical results, based on the end-to-end simulation of the SAR image formation process in different operational scenarios, highlight the relevance of this effect and the derived analytical description, especially in view of the demanding radiometric quality requirements imposed on future SAR images.

«Eyes burn words, words burn eyelids» (Imaginary potential of somatisms in the poetic picture of the world of Mykola Vinhranovskyi)

The article clarifies the peculiarities of the use of somatic units belonging to the general language dictionary; their imaginative potential is revealed in Mykola VinHranovskyi's poetic picture of the world. A wide range of figurative use of somatic lexemes is noted, the semantic specificity of their functioning is substantiated – due to the combination with ethnically marked words-images dream, hope, song, beauty, love, etc.; the introduction of original, unusually modeled tropeic, emotional and evaluative structures. Processes of image formation based on associative convergences of direct and figurative meanings of somatisms, external and internal portraiture of the lyrical "I", actualization of ethnic ideas about female beauty, beauty through the prism of the author's poetic language thinking and aestheticization of semantic plans of somatic lexical units are traced. The range of the figurative potential of somatisms in M. Vinhranovskyi's poetic picture of the world is extremely wide – from lexical-semantic to tropeic and ethnolinguistic paradigms. The key semantic vectors of its objectification are a combination of somatisms with ethnically marked words-images dream, hope, song, beauty, love, etc. The imagery of somatisms is revealed in the composition of originally modeled tropeic, emotive-evaluative, associatively expressed linguistic structures and through the prism of individual authorial aestheticization of the semantic plans of somatic lexical units. The frequency of the use of somatisms enables the characterization of figurative structures centered on "somatism" through chronological-worldview and ethnolinguistic, culturally oriented author's intentions, their objectification and influence on the general expressive plan of the entire expression. The full realization of the imaginative potential of somatisms is provided by contexts in which complex models combining several somatisms are represented. Such figurative models testify to the originality of M. Vinhranovskyi's linguistic thinking, his ability to find unexpected associative connections, revealing an individually modified and rich system of emotional and evaluative meaning plans of somatisms.

Efficient SAR Imaging Integrated With Autofocus via Compressive Sensing

In synthetic aperture radar (SAR) imaging, perturbations in the motion of the moving platform induce an undesired phase error due to imprecise knowledge of the motion, which results in the significant degradations in the quality of SAR images. In this paper, we present an efficient compressive sensing (CS)-based SAR imaging integrated with autofocus technique. The novel approach is based on an estimation-theoretic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm-based approach coupled with Tikhonov-type regularization which combines an observation model of the SAR image formation process with the CS reconstruction problem of the SAR image regarding the sparsity. The optimization problem derived by considering spatially variant phase errors along azimuth domain and the dataset sampled at low rates can be effectively addressed by an efficient iterative method, wherein each iteration both SAR image formation and phase error correction are simultaneously carried out. The simulations and experimental results are presented to validate the effectiveness of the proposed method in terms of reliable image recovery and efficient computational complexity.

Emotions and Cognitions When Reading Online Reviews: Effect on Tourism Service Image

The purpose of this study is to examine how online reviews from other users in a tourism service website generate emotions and cognitions that contribute to the formation of the image of a tourism service. We used a real website of a rural destination as a tourism service. We tested the proposed conceptual model using structural equation modeling. Our results show a correspondence between consumers’ thoughts and the cognitive dimension of image, as well as between the emotions expressed by subjects and the affective dimension of image. Results confirm that the image of a tourism service is an important predictor of tourism service preferences. This overall image influences consumers' future intentions, not only to buy the service but also to recommend it to other consumers. In summary, we propose and test a model that shows how online reviews significantly influence image formation and subsequent consumer behaviors. To date, little is known about to what extent online reviews are useful to generate cognitive and emotional responses in potential tourists. Consequently, understanding its role in a tourist's decision making process, and assessing the relationships between utility of online reviews, emotions, cognitions and image towards the tourism service will make both scientific and practical contributions. While contributing to the body of knowledge on image formation process, our results are also relevant for public administrations and tourism marketing managers.

Large steps in inverse rendering of geometry

Inverse reconstruction from images is a central problem in many scientific and engineering disciplines. Recent progress on differentiable rendering has led to methods that can efficiently differentiate the full process of image formation with respect to millions of parameters to solve such problems via gradient-based optimization. At the same time, the availability of cheap derivatives does not necessarily make an inverse problem easy to solve. Mesh-based representations remain a particular source of irritation: an adverse gradient step involving vertex positions could turn parts of the mesh inside-out, introduce numerous local self-intersections, or lead to inadequate usage of the vertex budget due to distortion. These types of issues are often irrecoverable in the sense that subsequent optimization steps will further exacerbate them. In other words, the optimization lacks robustness due to an objective function with substantial non-convexity. Such robustness issues are commonly mitigated by imposing additional regularization, typically in the form of Laplacian energies that quantify and improve the smoothness of the current iterate. However, regularization introduces its own set of problems: solutions must now compromise between solving the problem and being smooth. Furthermore, gradient steps involving a Laplacian energy resemble Jacobi's iterative method for solving linear equations that is known for its exceptionally slow convergence. We propose a simple and practical alternative that casts differentiable rendering into the framework of preconditioned gradient descent. Our pre-conditioner biases gradient steps towards smooth solutions without requiring the final solution to be smooth. In contrast to Jacobi-style iteration, each gradient step propagates information among all variables, enabling convergence using fewer and larger steps. Our method is not restricted to meshes and can also accelerate the reconstruction of other representations, where smooth solutions are generally expected. We demonstrate its superior performance in the context of geometric optimization and texture reconstruction.