Low-cost Imaging Research Articles

A Low-Cost Modulated Laser-Based Imaging System Using Square Ring Laser Illumination for Depressing Underwater Backscatter

Underwater vision data facilitate a variety of underwater operations, including underwater ecosystem monitoring, topographical mapping, mariculture, and marine resource exploration. Conventional laser-based underwater imaging systems with complex system architecture rely on high-cost laser systems with high power, and software-based methods can not enrich the physical information captured by cameras. In this manuscript, a low-cost modulated laser-based imaging system is proposed with a spot in the shape of a square ring to eliminate the overlap between the illumination light path and the imaging path, which could reduce the negative effect of backscatter on the imaging process and enhance imaging quality. The imaging system is able to achieve underwater imaging at long distance (e.g., 10 m) with turbidity in the range of 2.49 to 7.82 NTUs, and the adjustable divergence angle of the laser tubes enables the flexibility of the proposed system to image on the basis of application requirements, such as the overall view or partial detail information of targets. Compared with a conventional underwater imaging camera (DS-2XC6244F, Hikvision, Hangzhou, China), the developed system could provide better imaging performance regarding visual effects and quantitative evaluation (e.g., UCIQUE and IE). Through integration with the CycleGAN-based method, the imaging results can be further improved, with the UCIQUE increased by 0.4. The proposed low-cost imaging system with a compact system structure and low consumption of energy could be equipped with platforms, such as underwater robots and AUVs, to facilitate real-world underwater applications.

Research on real-time 3D imaging technology based on CMOS sensors

Abstract. With the continuous progress of science and technology, 3D imaging technology has been widely used in many fields, such as medical imaging, virtual reality and protection of cultural heritage. It can provide more information than traditional 2D images, greatly improving the accuracy and reliability of various applications. CMOS (Complementary Metal Oxide Semiconductor) sensors, as efficient and low-cost image sensors, have been widely used in image imaging and processing technologies in recent years. However, how to realize real-time and high-precision 3D imaging with CMOS sensors is still a technical challenge to be solved. Based on this background, this paper will focus on real-time 3D imaging techniques based on CMOS sensors through three topics. This paper will first introduce how CMOS sensors work, followed by the introduction of real-time 3D imaging technology, and finally the real-time 3D imaging technology based on CMOS sensors. The research in this paper will be valuable for the research and application of real-time 3D imaging technology based on CMOS sensors.

Short-term forecast of solar irradiance components using an alternative mathematical approach for the identification of cloud features

Solar energy technologies require precise solar forecasting to reduce power generation losses and protect equipment from irradiance fluctuations. This study introduces an alternative methodology for short-term forecasting of direct normal irradiance (DNI) and global horizontal irradiance (GHI) utilizing ground-based sky images captured by a single device. A low-cost all-sky imager (ASI) was developed, which implements an angular transformation and an optical flow technique to extract cloud features such as shape and velocity. A mathematical model calculates cloud transmittance based on pixel intensity, eliminating complex training steps. Results from a 30-day experimental campaign, incorporating diverse meteorological conditions, were compared against a secondary standard solarimetric station, a smart persistence model, and state-of-the-art approaches. The DNI forecast achieved an RMSE (relative error) of 46.79 W/m2 (11.99%) for 1-min intervals and 90.21 W/m2 (17.54%) for 10-min intervals, while GHI ranged from 31.73 W/m2 (4.68%) to 75.02 W/m2 (13.63%). Pearson correlation coefficients exceeded 0.9 overall, reaching 0.98 and 0.99 for the 1-min DNI and GHI forecasts, and 0.91 and 0.96 for the 10-min DNI and GHI forecasts, respectively, underscoring the system’s accuracy and robustness in complex meteorological scenarios.

Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer.

Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops.

Imaging of permeability defect distribution by electromagnetic tomography with hybrid L1 normand nuclear normpenalty terms.

Electromagnetic tomography (EMT), with the advantages of being non-contact, non-invasiveness, low cost, simple structure, and fast imaging speed, is a multi-functional tomography technique based on boundary measurement voltages to image the conductivity distribution within the sensing field. EMT is widely used in industrial and biomedical fields. Currently, there are few studies on the application of EMT in magnetic permeability materials, which makes it difficult to obtain high-quality reconstructed images due to its own properties that lead to obvious attenuation of electromagnetic waves during propagation, as well as the ill-posed and ill-conditioned characteristics of EMT. In this paper, a multi-feature objective function integrating L2 normregularization, L1 normregularization, and low-rank normregularization is proposed to solve the challenge of magnetic permeability material imaging. This approach emphasizes the smoothness and sparsity. The split Bregman algorithm is introduced to efficiently solve the proposed objective function by decomposing the complex optimization problem into several simple sub-task iterative schemes. In addition, a nine-coil planar array electromagnetic sensor was developed and a flexible modular EMT system was constructed. We use correlation coefficient and error coefficient as indicators to evaluate the performance of the proposed image reconstruction algorithm. The effectiveness of the proposed method in improving the reconstruction accuracy and robustness is verified through numerical simulations and experiments.

Implementation of the diagnostic capabilities of the CMOS sensor in the NIR environment, using 1070 nm interference filter and a conventional IR-pass filters set

NIR reflectography with silicon sensors (CMOS) is commonly acquired with 780 nm band-pass filters that allow the acquisition of clear images and high shutter speeds, while maintaining a low equipment cost. In this way, however, acquisition between 1000 nm and 1150nm-where the silicon sensor is still formally infrared sensitive-is in fact overhelmed by the stronger sensitivity in the infrared spectrum portion between 780 nm and 980 nm. Coupling a 1070 nm (± 5 nm) interferencial filter to an 87C nm IR pass filter (FHWH 850 nm) acquisitions in this portion of the NIR spectrum were carried out, witnessing a outstanding increase in visibility of underdrawings and pentimenti. A practical test of the effectiveness of the filters system mounted on the Nikon D800 IRUV was made, comparing the results with those obtained by the “Osiris” InGaAs detector by Opus Instruments. The comparison was performed on the “Deposition” (oil on panel) by Antonio Semino (1485–1555) in the collection of the Accademia Ligustica of Genoa, highlighting a significant qualitative proximity between the results obtained with the interference system and those with InGaAs detector, compared to the conventional acquisition with single IR long-pass filter.In addition, the 1070nm+87C filter system was used to increase the recognition capability of azurite pigments. This procedure widens the possibilities of first-impact diagnostics by means of low-cost and market available imaging systems based on commercial CMOS IRUV cameras..

Rapid simulation of knitted fabrics based on a ribbon yarn model

In the study, an innovative three-dimensional ribbon-based yarn model simulation technique is introduced, aimed at significantly enhancing the realism and speed of knitted fabric simulation through efficient model construction and rendering processes. The method successfully overcomes the limitations of traditional two-dimensional loop models and tubular yarn models, accurately capturing the textural details of knitted fabrics with a vivid three-dimensional representation. Experimental results demonstrate the outstanding performance of this simulation system in simulating the fuzz effects of yarn and in processing speed. This technology offers significant potential in textile design and production, notably in boosting design efficiency and accelerating new product development. By employing low-cost image capture methods, the study effectively reduces technical and financial barriers in knitted fabric simulation, enhancing its practical and industrial applicability.

Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications

Malaria is a major public health concern, causing significant morbidity and mortality globally. Monitoring the local population density and diversity of the vectors transmitting malaria is critical to implementing targeted control strategies. However, the current manual identification of mosquitoes is a time-consuming and intensive task, posing challenges in low-resource areas like sub-Saharan Africa; in addition, existing automated identification methods lack scalability, mobile deployability, and field-test validity. To address these bottlenecks, a mosquito image database with fresh wild-caught specimens using basic smartphones is introduced, and we present a novel CNN-based architecture, VectorBrain, designed for identifying the species, sex, and abdomen status of a mosquito concurrently while being efficient and lightweight in computation and size. Overall, our proposed approach achieves 94.44±2% accuracy with a macro-averaged F1 score of 94.10±2% for the species classification, 97.66±1% accuracy with a macro-averaged F1 score of 96.17±1% for the sex classification, and 82.20±3.1% accuracy with a macro-averaged F1 score of 81.17±3% for the abdominal status classification. VectorBrain running on local mobile devices, paired with a low-cost handheld imaging tool, is promising in transforming the mosquito vector surveillance programs by reducing the burden of expertise required and facilitating timely response based on accurate monitoring.

Estimation of Maize Water Requirements Based on the Low-Cost Image Acquisition Methods and the Meteorological Parameters

Estimation of Maize Water Requirements Based on the Low-Cost Image Acquisition Methods and the Meteorological Parameters

Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability. This review describes currently available AI and imaging systems, how a stable telemedicine infrastructure is crucial to AI implementation, and how successful ROP programs have been run in both low and middle-income countries and high-income countries. More work is needed in terms of validating AI systems with different populations with various low-cost imaging devices that have recently been developed. A sustainable and cost-effective ROP screening program is crucial in the prevention of childhood blindness.

Tactile Simultaneous Localization and Mapping Using Low-Cost, Wearable LiDAR

Tactile maps are widely recognized as useful tools for mobility training and the rehabilitation of visually impaired individuals. However, current tactile maps lack real-time versatility and are limited because of high manufacturing and design costs. In this study, we introduce a device (i.e., ClaySight) that enhances the creation of automatic tactile map generation, as well as a model for wearable devices that use low-cost laser imaging, detection, and ranging (LiDAR,) used to improve the immediate spatial knowledge of visually impaired individuals. Our system uses LiDAR sensors to (1) produce affordable, low-latency tactile maps, (2) function as a day-to-day wayfinding aid, and (3) provide interactivity using a wearable device. The system comprises a dynamic mapping and scanning algorithm and an interactive handheld 3D-printed device that houses the hardware. Our algorithm accommodates user specifications to dynamically interact with objects in the surrounding area and create map models that can be represented with haptic feedback or alternative tactile systems. Using economical components and open-source software, the ClaySight system has significant potential to enhance independence and quality of life for the visually impaired.

ICDDPM: Image-conditioned denoising diffusion probabilistic model for real-world complex point cloud single view reconstruction

Point cloud single-view reconstruction (PC-SVR) generates high-quality point clouds from low-cost 2D images, offering a cost-effective solution to the expensive and inefficient acquisition of point cloud for real-world scene typically achieved through expensive LiDAR systems. However, current models that generate point clouds from real-world 2D images (e.g. maritime vessels) still have shortcomings in terms of quality and model generalization. In this paper, we proposed a image-conditioned denoising diffusion probabilistic model (ICDDPM) for real-world complex point cloud single view reconstruction to address these issues. We re-designed the structure of classic diffusion model, use latent shape vectors to seamlessly integrate 2D image encoder, point cloud encoder, and conditional diffusion model, to cater PC-SVR task. By guiding the diffusion process with the 2D images, which serve as crucial conditional information, ICDDPM achieves end-to-end point cloud generation with superior quality. 2D images are also employed as input in the reverse diffusion process to further achieve point cloud generation. We conducted qualitative and quantitative experiments on synthetic dataset ShapeNet and real-world dataset PASCAL3D+ (focused on experiments of vessel point cloud data specifically). The results indicate that the ICDDPM model demonstrates superior performance compared to state-of-the-art models. It is capable of generating point clouds with a greater level of global and local details from various 2D image data. Additionally, the model exhibits strong generalization abilities and requires fewer computational resources.

Validation of In-House Imaging System via Code Verification on Petunia Images Collected at Increasing Fertilizer Rates and pHs.

In a production environment, delayed stress recognition can impact yield. Imaging can rapidly and effectively quantify stress symptoms using indexes such as normalized difference vegetation index (NDVI). Commercial systems are effective but cannot be easily customized for specific applications, particularly post-processing. We developed a low-cost customizable imaging system and validated the code to analyze images. Our objective was to verify the image analysis code and custom system could successfully quantify the changes in plant canopy reflectance. 'Supercascade Red', 'Wave© Purple', and 'Carpet Blue' Petunias (Petunia × hybridia) were transplanted individually and subjected to increasing fertilizer treatments and increasing substrate pH in a greenhouse. Treatments for the first trial were the addition of a controlled release fertilizer at six different rates (0, 0.5, 1, 2, 4, and 8 g/pot), and for the second trial, fertilizer solution with four pHs (4, 5.5, 7, and 8.5), with eight replications with one plant each. Plants were imaged twice a week using a commercial imaging system for fertilizer and thrice a week with the custom system for pH. The collected images were analyzed using an in-house program that calculated the indices for each pixel of the plant area. All cultivars showed a significant effect of fertilizer on the projected canopy size and dry weight of the above-substrate biomass and the fertilizer rate treatments (p < 0.01). Plant tissue nitrogen concentration as a function of the applied fertilizer rate showed a significant positive response for all three cultivars (p < 0.001). We verified that the image analysis code successfully quantified the changes in plant canopy reflectance as induced by increasing fertilizer application rate. There was no relationship between the pH and NDVI values for the cultivars tested (p > 0.05). Manganese and phosphorus had no significance with chlorophyll fluorescence for 'Carpet Blue' and 'Wave© Purple' (p > 0.05), though 'Supercascade Red' was found to have significance (p < 0.01). pH did not affect plant canopy size. Chlorophyll fluorescence pixel intensity against the projected canopy size had no significance except in 'Wave© Purple' (p = 0.005). NDVI as a function of the projected canopy size had no statistical significance. We verified the ability of the imaging system with integrated analysis to quantify nutrient deficiency-induced variability in plant canopies by increasing pH levels.

Low-cost two-dimensional digital image correlation system

A low-cost digital image correlation (DIC) system for measuring two-dimensional displacement fields is described. Using an off-the-shelf camera, lens, and open-source DIC analysis software, displacement fields in aluminum and steel dog-bone tensile specimens were measured. Correction for out-of-plane motion is used to obtain strains from the displacement fields. Proposed DIC strain measurement system is validated using conventional thin-film strain gauges for strains below 1%. Average strain measurements from the DIC system were found to be within 1% of the measurement obtained using strain gauges. For strains between 1% and 10%, strain histories from the DIC systems displayed excellent correlation with strain measurements obtained using an extensometer.

Design and development of a low-cost hyperspectral imaging setup via a machine-learningbased approach

This article presents a potential solution for developing a low-cost hyperspectral imaging (HSI) setup by preserving pertinent information acquired from a conventional hyperspectral imaging setup. Conventional hyperspectral images (HSI) of three different types of leaves, gongura (Hibiscus sabdariffa), amaranthus (Amaranthus viridis), and banana (Musa acuminata) were acquired with 204 wavelengths/bands. The spectra are processed using linear discriminant analysis (LDA) to find a set of signature wavelengths for leaf classification. Afterwards, 20 visible range wavelengths (440 to 700 nm) were found to be incorporated into a low-cost setup involving a monochromator, beam steering elements, and a smartphone camera with associated machine learning (ML) classifiers. For building the datasets, we extracted 90 spectra from the images captured using the conventional HSI setup under the full spectra range (397 nm to 1003 nm). Similarly, 90 spectra were extracted from the images captured from the low-cost setup under the 20 signature wavelengths. For further experimentation, we split the datasets into Dataset-A, containing 70% of the total spectra, and the remaining 30% in Dataset-B for HSI as well as low-cost setup. Dataset- B was reserved to evaluate the robustness of the classifiers on an unseen dataset. LDA surpasses the other classifiers in leaves classification for HSI as well as low-cost setup. For the HSI setup, LDA achieved a 100% average score for performance matrices (classification accuracy, precision, Recall, and F1-score) on dataset A as well as on dataset B. Moreover, for the low-cost setup, LDA achieved 98.33% ± 4.99% classification accuracy, 98.89% ± 3.33% precision, 98.33% ± 4.99% recall, and 98.22% ± 5.33% F1-score on dataset A. Additionally, LDA achieved 96.29% classification accuracy, 96.67% precision, 96.29% recall, and 96.28% F1-score for dataset B. The promising results indicated that the low-cost set-up closely emulates the HSI system’s performance in terms of performance metrics, delivering a low-cost HSI system for various applications in the future.

Material Inspection of Historical Built Heritage with Multi-Band Images: A Case Study of the Serranos Towers in Valencia

Built heritage materials assessment is an important task for planning and managing future conservation works. The uniqueness of each historical building makes reconnaissance operations more complex and specific for every single building. In the past, visual inspection and invasive techniques were widely used to investigate surface materials. Non-destructive techniques (NDTs) such as multi-band photogrammetry and remote sensing can help to assess the buildings without any contact with the investigated objects, restricting the disruptive tests on limited areas and reducing the testing time and costs of the surveys. This paper presents the results obtained using multi-band images acquired with a low-cost imaging solution after interchanging several filters, and the application of the principal components analysis (PCA) to recognize different materials of a significant historical monument. The Serranos Towers, built between 1392 and 1398, suffered several interventions in the past that affected their state of conservation with the replacement of different materials. The results of the study show the usefulness of applying PCA to distinguish different surface materials, often similar to the original ones, in a fast and efficient way to investigate and analyze our heritage legacy.

Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.

Canopy imaging offers a non-destructive, efficient way to objectively measure canopy size, detect stress symptoms, and assess pigment concentrations. While it is faster and easier than traditional destructive methods, manual image analysis, including segmentation and evaluation, can be time-consuming. To make imaging more widely accessible, it's essential to reduce the cost of imaging systems and automate the analysis process. We developed a low-cost imaging system with automated analysis using an embedded microcomputer equipped with a monochrome camera and a filter for a total hardware cost of ~USD 500. Our imaging system takes images under blue, green, red, and infrared light, as well as chlorophyll fluorescence. The system uses a Python-based program to collect and analyze images automatically. The multi-spectral imaging system separates plants from the background using a chlorophyll fluorescence image, which is also used to quantify canopy size. The system then generates normalized difference vegetation index (NDVI, "greenness") images and histograms, providing quantitative, spatially resolved information. We verified that these indices correlate with leaf chlorophyll content and can easily add other indices by installing light sources with the desired spectrums. The low cost of the system can make this imaging technology widely available.

Smartphone video imaging: A versatile, low-cost technology for food authentication

This study presents a low-cost smartphone-based imaging technique called smartphone video imaging (SVI) to capture short videos of samples that are illuminated by a colour-changing screen. Assisted by artificial intelligence, the study develops new capabilities to make SVI a versatile imaging technique such as the hyperspectral imaging (HSI). SVI enables classification of samples with heterogeneous contents, spatial representation of analyte contents and reconstruction of hyperspectral images from videos. When integrated with a residual neural network, SVI outperforms traditional computer vision methods for ginseng classification. Moreover, the technique effectively maps the spatial distribution of saffron purity in powder mixtures with predictive performance that is comparable to that of HSI. In addition, SVI combined with the U-Net deep learning module can produce high-quality images that closely resemble the target images acquired by HSI. These results suggest that SVI can serve as a consumer-oriented solution for food authentication.

(Invited) Surface State Modulation of Colloidal Quantum Dots and Construction of Quantum Dot Based Infrared Detectors

Near-infrared photodetection and imaging devices play an important role in fields such as biological detection, information communication, military meteorology, etc. Traditional imaging devices require integration with readout circuits, and the complex integration process limits the development of infrared imaging systems. Lead sulfide colloidal quantum dots have excellent photoelectric properties in the infrared region, with a tunable bandgap width ranging from 0.4 to 1.5 eV, and spectral response covering below 2400 nm. Lead sulfide quantum dot films exhibit high absorption coefficients in the infrared region and excellent carrier mobility. In recent years, the performance of infrared detectors based on quantum dot systems has rapidly improved, and they are expected to develop into a new type of infrared detection and imaging technology. The speaker's team has combined infrared quantum dots with visible light quantum dots to prepare a simple, low-cost, and efficient infrared upconversion imaging device. By using the method of inducing band bending through interface electron storage, the efficiency of carrier tunneling and photon conversion has been improved, and the application of this technology in biological imaging has been preliminarily demonstrated. This report will introduce the development of quantum dot infrared detection materials and devices in recent years, as well as the research progress of the speaker's team in this area.

Design and characterization of a low-cost particle image velocimetry system

Particle Image Velocimetry (PIV) is considered the gold standard technique for flow visualization. However, its cost (at least tens of thousands of dollars) can prove inhibitive in its standard form. This article presents an alternative design, leveraging off-the-shelf and open-source options for each key component involved: camera, laser module, optical components, tracer particles, and analysis software. Flow visualization is a crucial technique to connect theory to practice in teaching and researching fluid mechanics. Despite the ubiquity of this field within engineering curricula, many undergraduate institutions globally forego utilizing such equipment, given the barriers to setting it up. The availability of this low-cost alternative (∼$500) that can be built in-house offers a path forward. Characterization was done by visualizing the rotational flow generated by a magnetic stirrer in a cylindrical beaker. The velocity magnitude around the stirrer bar measured by the low-cost PIV system was compared to expected values calculated analytically. The percent difference was between 1–2% when the flow stayed two-dimensional but increased as the flow began developing into more of a 3-D flow. Repeatability varied no more than 6% between experiments. This platform holds the potential for reliable replication across institutions broadly.