Image Formation Process Research Articles

Sensitivity fields and parameter estimation from dielectric objects

The quantitative phase image formation process is posed as a problem of parameter estimation from intensity measurements. This approach is inclusive of traditional pixel-oriented imaging, where the sought parameters are the pixel values. The resulting optimization process to find the parameters is then seen to depend on the sensitivity field: this is the gradient of the scattered field with respect to the parameters, and it turns out to obey a scattering relationship that is analogous to that of the original scattered field. Examples are given from several regimes of scattering strength.

Stochastic image spectroscopy: a discriminative generative approach to hyperspectral image modelling and classification

This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.

Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques – a short review including new research

Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or ‘label-free’ imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive. Computational imaging denotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques, with an emphasis on Fourier ptychographic microscopy (FPM) and advanced x-ray microscopies. We next demonstrate with our own results computational imaging through FPM and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are explored. Furthermore, X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies and in vivo possibilities conclude the article.

In situ build surface topography determination in electron beam powder bed fusion

Electron optical imaging is the most promising process monitoring method in electron beam powder bed fusion. State of the art in modern machines is the installation of a single detector in the top center of the build chamber. Exemplary applications are the reconstruction of digital twins of manufactured parts to compare their dimensional accuracy or analysing the top surface of each layer to identify surface features like pores or material transport. Multi-detector systems are currently under research and have shown great potential in reconstructing the surface topography in situ. A recently developed ray tracing model, describing the image formation process, allows to formulate design guide lines for multi-detector systems and provides a method for the computation of the normal vector field of the build surface. This work utilizes the recent progress and presents a newly developed four-detector system and an updated computation chain, which enable build surface topography reconstruction in situ in every layer of a build process. The computation chain contains a normal integration algorithm, which employs Tikhonov regularization to cope with measurement irregularities. The integration method is validated with ex situ measured as-built surfaces. Additionally, first applications are demonstrated and connections to process parameter changes illustrated.

A photogrammetric approach for real‐time visual SLAM applied to an omnidirectional system

AbstractThe problem of sequential estimation of the exterior orientation of imaging sensors and the three‐dimensional environment reconstruction in real time is commonly known as visual simultaneous localisation and mapping (vSLAM). Omnidirectional optical sensors have been increasingly used in vSLAM solutions, mainly for providing a wider view of the scene, allowing the extraction of more features. However, dealing with unmodelled points in the hyperhemispherical field poses challenges, mainly due to the complex lens geometry entailed in the image formation process. To address these challenges, the use of rigorous photogrammetric models that appropriately handle the geometry of fisheye lens cameras can overcome these challenges. Thus, this study presents a real‐time vSLAM approach for omnidirectional systems adapting ORB‐SLAM with a rigorous projection model (equisolid‐angle). The implementation was conducted on the Nvidia Jetson TX2 board, and the approach was evaluated using hyperhemispherical images captured by a dual‐fisheye camera (Ricoh Theta S) embedded into a mobile backpack platform. The trajectory covered a distance of 140 m, with the approach demonstrating accuracy better than 0.12 m at the beginning and achieving metre‐level accuracy at the end of the trajectory. Additionally, we compared the performance of our proposed approach with a generic model for fisheye lens cameras.

Образ роду Ґалаґанів в історіографії кінця ХІХ – початку ХХ ст.

The purpose of the study is to conduct an analytical assessment of the Galagan noble-foreman family image formation and evolution process in historiography of the late 19th – early 20th centuries. The methodological basis of the work was formed by the basic principles of the historiographic image theory, aimed at identifying genesis, formation and evolution of understanding of the Galagans’ role in historiography. The following methods are also used in the work: historical, analytical, genetic, comparative, biographical, method of studying the products of activity.The scientific novelty is the fact that the Galagan family is first considered through the prism of the theory of the historiographical image. Conclusions. Through the efforts of a small but very active constellation of Ukrainian scientists, Galagan family was included in the scientific circulation. For three decades, their image has undergone significant transformations. Although Alexander Lazarevsky was quite critical of the Galagans, especially representatives of the first generations, the pages of the magazine «Kievskaia starina», on the contrary, showed mainly positive aspects of their activities, special attention was paid to the last representative of the family, Grigory Galagan Jr. The publication of Andronik Stepovych had a specific structure and was based on sources of various genres: from memoirs to an unpublished article authored by Grigory Galagan Jr., which was devoted to the history of his family. The publisher allowed the reader to see how Gregory Galahan Jr. assessed his ancestors and get acquainted with the memories of his son Paul. The chapter on Gregory Galahan Jr., which Stepovich wrote personally, is evidence of the author’s extremely respectful attitude towards his hero. The images of the father and son of the Galagans, which are idealistic in their content, are significantly different against the background of less emotional and ambiguous assessments of their ancestors. Vadim Modzalevsky in his work depersonalized Galaganov and equated them to ordinary representatives of the nobility of Left-Bank Ukraine. But this is due to the restrictions imposed on the genre of his work, a genealogical essay, which was supposed to contain only basic information about members of noble families and the connections between them. The most complete, multifaceted images had the first and penultimate male members of the genus. The image of Ignat Galagan in all works is mostly negative, his immoral actions, greed, low intellectual level are brought to the fore, although evidence of personal courage and the ability to «make his way in life» also take place. Grigory Galagan Jr., on the contrary, receives an extremely positive assessment, his image is used as a symbol of Ukrainian patriotism and a role model. Other members of the family are presented in a much less vivid form. We can say that they are presented as authoritative, but typical Ukrainian landowners having both positive and negative features.

Res-U2Net: untrained deep learning for phase retrieval and image reconstruction

Conventional deep learning-based image reconstruction methods require a large amount of training data, which can be hard to obtain in practice. Untrained deep learning methods overcome this limitation by training a network to invert a physical model of the image formation process. Here we present a novel, to our knowledge, untrained Res-U2Net model for phase retrieval. We use the extracted phase information to determine changes in an object’s surface and generate a mesh representation of its 3D structure. We compare the performance of Res-U2Net phase retrieval against UNet and U2Net using images from the GDXRAY dataset.

RSUIGM: Realistic Synthetic Underwater Image Generation with Image Formation Model

In this paper, we propose to synthesize realistic underwater images with a novel image formation model, considering both downwelling depth and line of sight (LOS) distance as cue and call it as Realistic Synthetic Underwater Image Generation Model, RSUIGM. The light interaction in the ocean is a complex process and demands specific modeling of direct and backscattering phenomenon to capture the degradations. Most of the image formation models rely on complex radiative transfer models and in-situ measurements for synthesizing and restoration of underwater images. Typical image formation models consider only line of sight distance z and ignore downwelling depth d in the estimation of effect of direct light scattering. We derive the dependencies of downwelling irradiance in direct light estimation for generation of synthetic underwater images unlike state-of-the-art image formation models. We propose to incorporate the derived downwelling irradiance in estimation of direct light scattering for modeling the image formation process and generate realistic synthetic underwater images with the proposed RSUIGM, and name it as RSUIGM dataset . We demonstrate the effectiveness of the proposed RSUIGM by using RSUIGM dataset in training deep learning based restoration methods. We compare the quality of restored images with state-of-the-art methods using benchmark real underwater image datasets and achieve improved results. In addition, we validate the distribution of realistic synthetic underwater images versus real underwater images both qualitatively and quantitatively. The proposed RSUIGM dataset is available here.

Transport-of-intensity model for single-mask x-ray differential phase contrast imaging

X-ray phase contrast imaging holds great promise for improving the visibility of light-element materials such as soft tissues and tumors. The single-mask differential phase contrast imaging method stands out as a simple and effective approach to yield differential phase contrast. In this work, we introduce a model for a single-mask phase imaging system based on the transport-of-intensity equation. Our model provides an accessible understanding of signal and contrast formation in single-mask x-ray phase imaging, offering a clear perspective on the image formation process, for example, the origin of alternate bright and dark fringes in phase contrast intensity images. Aided by our model, we present an efficient retrieval method that yields differential phase contrast imagery in a single acquisition step. Our model gives insight into the contrast generation and its dependence on the system geometry and imaging parameters in both the initial intensity image as well as retrieved images. The model validity as well as the proposed retrieval method are demonstrated via both experimental results on a system developed in house as well as Monte Carlo simulations. In conclusion, our work not only provides a model for an intuitive visualization of image formation but also offers a method to optimize differential phase imaging setups, holding tremendous promise for advancing medical diagnostics and other applications.

Simplifying vein detection for intravenous procedures: A comparative assessment through near‐infrared imaging system

AbstractThe intravenous (IV) injection procedure can be a challenging task, especially for individuals with thin veins, obesity, or patients with damaged and pigmented skin. Therefore, the IV procedure necessitates a portable medical device that can be used for academic demonstrations to train medical students or by health care professionals to perform venipuncture. Vein visualization with a vein detector is principally based on the interaction of blood components with wavelengths of the electromagnetic spectrum (EMS). In this paper, we first present the process of image formation in the spectral band of the near‐infrared region (NIR) of EMS. Then, we introduce the image acquisition system with image processing to extract the veins as a noninvasive vein detection method. A Raspberry Pi (Model 4B), along with a night vision camera, serves as an image acquisition tool to capture skin area illuminated by NIR. Following this, the data is transferred to the laptop where it can be filtered and processed using Python image processing tools before being viewed on the monitor. The results achieved through the device are quite encouraging, as the image recognition between veins and adjacent tissues from the skin sample can be clearly marked. The functionality, accuracy, and simplicity associated with this vein detection system make it a potential device for IV placement and the morphological study of disease detection.

Image analysis and resolution for detection-based synthetic-aperture passive source localization

This paper follows a detection-theoretic approach for using synthetic-aperture measurements, made at multiple moving passive receivers, in order to form an image showing the locations of stationary sources that are radiating unknown electromagnetic or acoustic waves. The paper starts with a physics-based model for the propagating fields, and, following the general approach of McWhorter et al (2023 arXiv:2302.06816, IEEE Open J. Signal Process. 4 437–51), derives a detection statistic that is used for the image formation. This detection statistic is a quadratic function of the data. Each point in the scene is tested as a possible hypothesized location for a source, and the detection statistic is plotted as a function of location. Because this image formation process is nonlinear, the standard linear methods for determining resolution cannot be applied. This paper shows how to analyze the detection image by first writing the noiseless image as a coherent sum of shifted complex ambiguity functions of the source waveform. The paper then develops a technique for calculating image resolution; resolution is found to depend on the sensor-source geometry and also on the properties (bandwidth and temporal duration) of the source waveform. Optimal filtering of the image is given, but a simple example suggests that optimal filtering may have little effect. Analysis is also given for the case in which multiple sources are present.

Numerical simulation of image formation in an optical device in the problem of space monitoring

Space monitoring systems are being developed for space object and debris observation. There are the following problems in this area: optimizing the operation of existing optoelectronic devices, locating new devices, analyzing the monitoring results of outer space. It is necessary to use modeling in order to solve these problems. In this regard, the authors aimed to investigate the process of image formation on a photodetector under specified observation conditions. A mathematical model of image formation was constructed and a software implementation of this model was developed. Synthetic images of the night sky and images of the trajectories of space objects under given conditions were formed. The obtained results were compared with real images from telescopes.

DGD-cGAN: A dual generator for image dewatering and restoration

Underwater images are usually covered with a blue–greenish colour cast, making them distorted, blurry or low in contrast. This phenomenon occurs due to the light attenuation given by the scattering and absorption in the water column. In this paper we present an image dewatering approach motivated upon the observation that the image formation model can be used to drive the learning process by constraining the loss function and making used of paired data. To this end, we employ a conditional generative adversarial network (cGAN) with two generators. Our Dual Generator Dewatering cGAN (DGD-cGAN) removes the haze and colour cast induced by the water column and restores the true colours of underwater scenes whereby the effects of various attenuation and scattering phenomena that occur in underwater images are tackled by the two generators. The first generator takes as input the underwater image and predicts the dewatered scene, while the second generator learns the underwater image formation process by implementing a custom loss function based upon the transmission and the veiling light components of the image formation model. Extensive experiments show that DGD-cGAN consistently delivers a margin of improvement as compared with state-of-the-art methods on several widely available datasets.

A Systematic Knowledge of Nanoparticles and Nanotechnology - A Review

The main aim is to describe the journey to beautiful and useful word Nano. Nano stands for one billionth. In science & technology, Nano stands for one billionth of meter, 1nm-10-9 m. Nanoparticles and nanotechnology words are relatively new but nanoparticles themselves has been around and studied long before the word were used. The beautiful colours of stained glass windows are a result of the presence of small metal oxide clusters in glass, having a size comparable to the wavelength of light. Particles of different sizes scatter different wavelength of light, imparting different colours to the glass. In photography the small colloidal particles of silver are a part of the processes of image formation and water at ambient temperature consists of clusters of hydrogen bonded water molecules. The description of variation of properties of nanoparticle with size, practical application of nanotechnology, uses in defence and biotechnology and as well as disadvantage of nanotechnology are given in this article.

Organizacijos įvaizdžio vertinimas UAB „Baltic Agro“ vartotojų požiūriu

The article examines the theoretical aspects of the formation of the organization's image. It emphasizes the importance of image in today's business, emphasizing that only by gaining the image of a supportive organization, companies gain a strong advantage in the market. The process of image formation is distinguished, consisting of the internal culture of the organization, employee competencies, product value, manager's behavior, business reputation, social behavior of the organization, visual image and image of the consumers themselves. A questionnaire survey method was selected for the empirical study and a probabilistic method of selecting subjects was applied – random selection. The analysis of the survey results enabled to assess the JSC Baltic Agro company's image based on the distinguished image formation factors and the identified elements of the organization's image. Keywords: image of organization, external image, internal image, elements of the image, factors of image formation.

Motivations, risks, and constraints: An analysis of affective and cognitive images for cannabis tourism in Canada

The aim of this study was to explore Canada's image as a cannabis tourism destination and investigate how tourists' motivations, perceived risks, and travel constraints affect their intentions to visit Canada for leisure cannabis consumption. By examining the image formation process from a pre-travel perspective, this study investigated the impacts of motivation, perceived risks, and travel constraints on the affective and cognitive images that contribute to the formation of a pre-travel image and visiting intention for cannabis tourism.Empirical results indicated that potential cannabis tourists' visiting intentions were influenced more by their affective image towards Canada than their cognitive image. Furthermore, perceived risk fully mediated the relationship between cognitive image and visiting intention. These findings provide valuable insights for local governments, destination marketing groups, and businesses interested in developing the cannabis tourism market.

Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar

Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods. To help meet these challenges, we design an analysis-by-synthesis optimization that leverages recent advances in neural rendering to perform coherent SAS imaging. Our optimization enables us to incorporate physics-based constraints and scene priors into the image formation process. We validate our method on simulation and experimental results captured in both air and water. We demonstrate both quantitatively and qualitatively that our method typically produces superior reconstructions than existing approaches. We share code and data for reproducibility.

A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality.

Quantifying the colors of objects is useful in a wide range of applications, including medical diagnosis, agricultural monitoring, and food safety. Accurate colorimetric measurement of objects is a laborious process normally performed through a color matching test in the laboratory. A promising alternative is to use digital images for colorimetric measurement, due to their portability and ease of use. However, image-based measurements suffer from errors caused by the non-linear image formation process and unpredictable environmental lighting. Solutions to this problem often perform relative color correction among multiple images through discrete color reference boards, which may yield biased results due to the lack of continuous observation. In this paper, we propose a smartphone-based solution, that couples a designated color reference board with a novel color correction algorithm, to achieve accurate and absolute color measurements. Our color reference board contains multiple color stripes with continuous color sampling at the sides. A novel correction algorithm is proposed to utilize a first-order spatial varying regression model to perform the color correction, which leverages both the absolute color magnitude and scale to maximize the correction accuracy. The proposed algorithm is implemented as a "human-in-the-loop" smartphone application, where users are guided by an augmented reality scheme with a marker tracking module to take images at an angle that minimizes the impact of non-Lambertian reflectance. Our experimental results show that our colorimetric measurement is device independent and can reduce up to 90% color variance for images collected under different lighting conditions. In the application of reading pH values from test papers, we show that our system performs 200% better than human reading. The designed color reference board, the correction algorithm, and our augmented reality guiding approach form an integrated system as a novel solution to measure color with increased accuracy. This technique has the flexibility to improve color reading performance in systems beyond existing applications, evidenced by both qualitative and quantitative experiments on example applications such as pH-test reading.

COMPARISON OF TWO MATHEMATICAL MODELS FOR FISHEYE CAMERAS APPLIED IN THE ORB-SLAM

Abstract. Fisheye lens cameras are becoming increasingly popular for vSLAM applications due to their wide field of view (FoV), providing more features to be tracked in a single image shot. However, the complex lens geometry involved in the image formation process still limits their full potential, especially when points in the hyperhemispherical field are unmodeled. In this paper, we compare two adaptations of ORB-SLAM for fisheye lens cameras, considering the use of the rigorous projection model (equisolid-angle) versus the use of the generic projection model (EUCM). The ORB-SLAM versions were adapted for real-time processing on the Nvidia Jetson TX2 board. The experiment was conducted using hyperhemispherical images obtained with a Ricoh Theta S camera. Our results showed that the trajectory estimated with the equisolid-angle ORB-SLAM had smaller discrepancies, compared to the reference trajectory, than the EUCM ORB-SLAM. This suggests that a rigorous photogrammetric model with a suitable treatment of hyperhemispherical points is beneficial for trajectory estimation.

Reconstructing the image scanning microscopy dataset: an inverse problem

Confocal laser-scanning microscopy (CLSM) is one of the most popular optical architectures for fluorescence imaging. In CLSM, a focused laser beam excites the fluorescence emission from a specific specimen position. Some actuators scan the probed region across the sample and a photodetector collects a single intensity value for each scan point, building a two-dimensional image pixel-by-pixel. Recently, new fast single-photon array detectors have allowed the recording of a full bi-dimensional image of the probed region for each scan point, transforming CLSM into image scanning microscopy (ISM). This latter offers significant improvements over traditional imaging but requires an optimal processing tool to extract a super-resolved image from the four-dimensional dataset. Here we describe the image formation process in ISM from a statistical point of view, and we use the Bayesian framework to formulate a multi-image deconvolution problem. Notably, the single-photon detector suffers exclusively from the photon shot noise, enabling the development of an effective likelihood model. We derive an iterative likelihood maximization algorithm and test it on experimental and simulated data. Furthermore, we demonstrate that the ISM dataset is redundant, enabling the possibility of obtaining reconstruction sampled at twice the scanning step. Our results prove that in ISM, under appropriate conditions, the Nyquist–Shannon sampling criterium is effectively relaxed. This finding can be exploited to speed up the acquisition process by a factor of four, further improving the versatility of ISM systems.