Imaging Algorithm Research Articles

An encryption algorithm for multiple medical images based on a novel chaotic system and an odd-even separation strategy

The continuous evolution of information technology underscores the growing emphasis on data security. In the realm of medical imaging, various diagnostic images represent the privacy of individuals, and the potential repercussions of their unauthorized disclosure are substantial. Therefore, this study introduces a novel chaotic system (TLCMCML) and employs it to propose a multi-image medical image encryption algorithm. To simultaneously augment security and optimize encryption efficiency, we undertake a dual-pronged approach. Firstly, we identify the regions of interest (ROIs) within individual medical images, subsequently applying an independent scrambling technique based on an odd-even interleaving configuration. Secondly, we integrate all medical images through horizontal concatenation, forming a comprehensive large-scale image, upon which we implement a synchronized bit-level permutation-diffusion encryption mechanism. Following extensive testing, enhancements were verified across various metrics including information entropy analysis, adjacent pixel correlation examination, differential attack simulations, and robustness assessments, thereby attesting to the exceptional encryption efficacy of the proposed algorithm.

Adaptive enhancement algorithm for low-illumination images of welding shops based on improved multi-scale Retinex with color restoration

Adaptive enhancement algorithm for low-illumination images of welding shops based on improved multi-scale Retinex with color restoration

Numerical analysis of mechanical properties at the internal interface of SFP material using a digital image algorithm

Numerical analysis of mechanical properties at the internal interface of SFP material using a digital image algorithm

Explainable analysis of infrared and visible light image fusion based on deep learning

Explainability is a very active area of research in machine learning and image processing. This paper aims to investigate the explainability of visible light and infrared image fusion technology in order to enhance the credibility of model understanding and application. Firstly, a multimodal image fusion model was proposed based on the advantages of convolutional neural networks (CNN) for local context extraction and Transformer global attention mechanism. Secondly, to enhance the explainability of the model, the Delta Debugging Fuse Image (DDFImage) algorithm was employed for generating local explanatory information. Finally, we gain deeper insights into the internal workings of the model through feature importance analysis of the generated explanatory fusion images. Comparative analysis with other explainability algorithms demonstrates the superior performance of our algorithm. This comprehensive approach not only improves the explainability of the model but also provides more reference for practical application of the model.

Real-Time Quantification of Gas Leaks Using a Snapshot Infrared Spectral Imager.

We describe the various steps of a gas imaging algorithm developed for detecting, identifying, and quantifying gas leaks using data from a snapshot infrared spectral imager. The spectral video stream delivered by the hardware allows the system to combine spatial, spectral, and temporal correlations into the gas detection algorithm, which significantly improves its measurement sensitivity in comparison to non-spectral video, and also in comparison to scanning spectral imaging. After describing the special calibration needs of the hardware, we show how to regularize the gas detection/identification for optimal performance, provide example SNR spectral images, and discuss the effects of humidity and absorption nonlinearity on detection and quantification.

Improving ICP-Based Scanning Sonar Image Matching Performance Through Height Estimation of Feature Point Using Shaded Area

This study presents an innovative method for estimating the height of feature points through shaded area analysis, to enhance the performance of iterative closest point (ICP)-based algorithms for matching scanning sonar images. Unlike other sensors, such as forward looking sonar (FLS) or BlueView, scanning sonar has an extended data acquisition period, complicating data collection while in motion. Additionally, existing ICP-based matching algorithms that rely on two-dimensional scanning sonar data suffer from matching errors due to ambiguities in the nearest-point matching process, typically arising when the feature points demonstrate similarities in size and spatial arrangement, leading to numerous potential connections between them. To mitigate these matching ambiguities, we restrict the matching areas in the two images that need to be aligned. We propose two strategies to limit the matching area: the first utilizes the position and orientation information derived from the navigation algorithm, while the second involves estimating the overlapping region between the two images through height assessments of the feature points, facilitated by shaded area analysis. This latter strategy emphasizes preferential matching based on the height information obtained. We propose integrating these two approaches and validate the proposed algorithm through simulations, experimental basin tests, and real-world data collection, demonstrating its effectiveness.

An effective feature selection approach based on hybrid Grey Wolf Optimizer and Genetic Algorithm for hyperspectral image

Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains an NP-hard challenge, as existing swarm intelligence and evolutionary algorithms (SIEAs) often suffer from limited exploration capabilities or susceptibility to local optima, particularly in high-dimensional scenarios. To address these challenges, we propose GWOGA, a novel hybrid algorithm that combines Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA), aiming to achieve an effective balance between exploration and exploitation. The innovation of GWOGA lies in three core strategies: (1) chaotic map and Opposition-Based Learning (OBL) for uniformly distributed population initialization, enhancing diversity and mitigating premature convergence; (2) elite learning strategy to prioritize high-ranking solutions, strengthening the search hierarchy and efficiency; and (3) a hybrid optimization mechanism where GWO ensures rapid early-stage convergence, while GA refines global search in later stages to escape local optima. Experiments on three benchmark HSIs (i.e., Indian Pines, KSC, and Botswana) demonstrate that GWOGA outperforms state-of-the-art algorithms, achieving higher classification accuracy with fewer selected bands. The results highlight GWOGA’s robustness, generalizability, and potential for real-world applications in HSI FS.

Application Research of Vision-Guided Grinding Robot for Wheel Hub Castings

The combination of vision and robotic grinding technology provides robots with “visual perception” capabilities that enable them to accurately locate the area to be ground and perform the grinding tasks efficiently. Based on the rough grinding requirements for wheel hub burrs proposed by a casting company, this paper investigates the application of a vision-guided grinding robot in treating burrs on wheel hub castings. First, through vision system calibration, the conversion from pixel coordinate system to robot base coordinate system is implemented, thus ensuring that the subsequently extracted burr point coordinates can be correctly mapped to the robot’s operational coordinate system. Next, the images of the burrs on wheel hub castings are collected and processed. All the burr points are extracted by applying image algorithms. In order to improve grinding accuracy, a height error compensation model is established to adjust the coordinates of the 2D-pixel points; the coordinate error after compensation was reduced by 58.33%. Subsequently, the compensated burr point trajectories are optimized by utilizing an intelligent optimization algorithm to generate the shortest grinding path. Through experimental analysis of the relationship between spindle speed and surface roughness, a grinding trajectory simulation model is constructed, and the simulation results are integrated into the robot system. Finally, actual wheel hub burr grinding experiments are performed to validate the effectiveness and practicality of the proposed solution.

Change in management for digital subtraction angiography-identified false-positive traumatic vertebral artery injury.

For patients with suspected traumatic vertebral artery injury (TVAI), CT angiography (CTA) is the first-line screening modality. Digital subtraction angiography (DSA) serves as the confirmatory diagnostic imaging, and is the gold standard for cerebrovascular injury assessment, due to its higher sensitivity and specificity. Among patients with TVAI based on CTA who have undergone follow-up DSA, this study aims to investigate how diagnostic information with additional imaging affects clinical management. A retrospective review was conducted over 7 years (2016-2023) at a level 1 trauma center for TVAI patients undergoing both CTA and DSA. Pre- and post-DSA approaches to TVAI management were compared and summarized using propensity-score matched analysis. Among the 69 patients studied, 24.6% were determined to have false-positive TVAI after DSA. The rate of change in management after DSA was significantly different across DSA+ and DSA- cohorts (p = 0.02). The likelihood of a change in management in patients with based on outcome of the DSA was significant (p = 0.03) in the propensity-matched cohort. On average, 3 (NNI = 3.2) patients would need to receive a DSA for one additional patient to undergo a change in management. This study demonstrates that, despite initial CTA imaging suggestive of TVAI, follow-up DSA imaging negative for TVAI has a significant impact on changing clinical management, including cessation of antithrombotic agents. Thus, for TVAI patients, DSA may be considered in the diagnostic workup for select patients with positive CTA. Larger cohort analyses are needed to refine imaging algorithms and optimize clinical outcomes for TVAI patients.

Sequential Multimodal Underwater Single-Photon Lidar Adaptive Target Reconstruction Algorithm Based on Spatiotemporal Sequence Fusion

For the demand for long-range and high-resolution target reconstruction of slow-moving small underwater targets, research on single-photon lidar target reconstruction technology is being carried out. This paper reports the sequential multimodal underwater single-photon lidar adaptive target reconstruction algorithm based on spatiotemporal sequence fusion, which has strong information extraction and noise filtering ability and can reconstruct the target depth and reflective intensity information from complex echo photon time counts and spatial pixel relationships. The method consists of three steps: data preprocessing, sequence-optimized extreme value inference filtering, and collaborative variation strategy for image optimization to achieve high-quality target reconstruction in complex underwater environments. Simulation and test results show that the target reconstruction method outperforms the current imaging algorithms, and the built single-photon lidar system achieves underwater lateral and distance resolution of 5 mm and 2.5cm@6AL, respectively. This indicates that the method has a great advantage in sparse photon counting imaging and possesses the capability of underwater target imaging under the background of strong light noise. It also provides a good solution for underwater target imaging of small slow-moving targets with long-distance and high-resolution.

Integrated of Hyperspectral Imaging and Machine Learning Algorithms for Nondestructive Detection of Therapeutic Properties of Plants.

The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making it impossible to analyze their quality analysis online. Performance of hyperspectral imaging (HSI) integrated with intelligent techniques to overcome these problems was investigated in this research. For this purpose, three classification methods-support vector machine, random forest (RF), and extreme gradient boosting-were studied for the classification of plants in three classes of medicinal, edible, and ornamental for the organs of leaf, stem, flower, and root. The medicinal effects of the plant organs were determined by measuring different biochemical properties of the organs. The spectral reflectance of the samples was used to train and test the classification methods in which output targets were the plant types. The results showed that amounts of the biochemical factors except oil content of the medicinal plants were higher than the other types of plants. Further, the biochemical factors of flowers and leaves were higher than the other organs indicating that the most therapeutic effect of the plants is through the flowers and leaves. Using HSI, a similar spectral trend was appeared in each organ, whereas it was different among the organs. Using the RF as the best method (precision and accuracy were higher than 0.95), the lowest misclassification rates were related to the stem and leaf datasets, indicating that these two organs were most suitable to classify the plants aromatically. The most misclassifications of the organs were occurred between medicinal and edible plants related to the spectra having higher correlations with flavonoid, phenol, and antioxidant compounds. Overall, the misclassification rates were negligible, and thus, the methods developed in this study can be used online in postharvest processes of the medicinal plants.

Treatment of Acne With a 1726 nm Laser, Air Cooling, and Real-Time Temperature Monitoring, Software-Assisted Power Adjustment to Achieve a Temperature Endpoint With Selective Sebaceous Gland Photothermolysis.

This work highlights the methods used to develop a multi-pulse 1726 nm laser system combined with bulk air-cooling for selective sebaceous gland (SG) photothermolysis using thermal imaging and software algorithms. This approach enables treating to a desired tissue temperature and depth to provide a safe, effective, reproducible, and durable treatment of acne. We designed and built a 1726 nm laser system with a 40 W maximum power output, a highly controlled air-cooling device, and a thermal camera in the handpiece, which permits real-time temperature monitoring of the epidermis. IRB-approved safety and efficacy trials demonstrated SG damage at depth, resulting in safe, efficacious, and durable clinical outcomes. Bioheat transfer and light transport modeling confirmed that the pulsing protocols could produce therapeutic temperatures at various SG depths, while protecting the epidermis and dermis with bulk air-cooling. Similarly, we employed clinical observations and photothermal modeling to identify pain mitigation opportunities while maintaining therapeutic efficacy. Biopsies were subsequently taken for histological evaluation. Clinical and histological data, confirmed with modeling, demonstrated that multi-pulse laser delivery with bulk air-cooling selectively increased SG temperature compared to surrounding dermis and at depths unachievable by a single pulse. Subjects showed an average 71% ILC reduction at 3 months posttreatment. We identified two different pulsing protocols with similar selective photothermolysis (SP) of the SG with very different pain responses. Thus, changing the pulsing protocols allowed for pain mitigation and eliminated the need for injectable anesthetic. Histology confirmed the selective damaging of the SG at depth and the preservation of the surrounding dermis and the epidermis. The multi-pulse 1726 nm laser with bulk air-cooling, thermal monitoring, treat-to-temperature (and depth) control, and a unique pulsing protocol, is capable of selectively damaging SGs at depth without damage to the surrounding dermis or the epidermis. The system offers two different protocols that were developed with different levels of discomfort allowing for two different methods for pain mitigation (injectable vs. topical anesthesia).

Single-shot x-ray ptychography as a structured illumination method.

Single-shot ptychography is a quantitative phase imaging method wherein overlapping beams of light arranged in a grid pattern simultaneously illuminate a sample, allowing a full ptychographic dataset to be collected in a single shot. It is primarily used at optical wavelengths, but there is interest in using it for x-ray imaging. However, constraints imposed by x-ray optics have limited the resolution achievable to date. In this work, we reinterpret single-shot ptychography as a structured illumination method by viewing the grid of beams as a single, highly structured illumination function. Pre-calibrating this illumination and reconstructing single-shot data using the randomized probe imaging algorithm allows us to account for the overlap and coherent interference between the diffraction arising from each beam. We achieve a resolution 3.5 times finer than the numerical aperture-based limit imposed by traditional algorithms for single-shot ptychography. We argue that this reconstruction method will work better for most single-shot ptychography experiments and discuss the implications for the design of future single-shot x-ray microscopes.

A Novel Land Surface Temperature Retrieval Algorithm for SDGSAT-1 Images

A Novel Land Surface Temperature Retrieval Algorithm for SDGSAT-1 Images

A sector fast encryption algorithm for color images based on one-dimensional composite sinusoidal chaos map.

Images are important information carriers in our lives, and images should be secure when transmitted and stored. Image encryption algorithms based on chaos theory emerge in endlessly. Based on previous various chaotic image fast encryption algorithms, this paper proposes a color image sector fast encryption algorithm based on one-dimensional composite sinusoidal chaotic mapping. The main purpose of this algorithm is to improve the encryption and decryption speed of color images and improve the efficiency of image encryption in the big data era. First, four basic chaos maps are combined in pairs and added with sine operations. Six one-dimensional composite sinusoidal chaos maps (CSCM) were obtained. Secondly, select the two best chaotic mappings LCS and SCS. The randomness of these two chaotic mappings was verified through Lyapunov index and NIST SP 800-22 randomness tests. Thirdly, the encryption process is carried out according to the shape of a traditional Chinese fan, and the diffusion and scrambling of each pixel of the image are performed in parallel. This greatly improves encryption speed. When diffusing, changing the value of one pixel can affect the values of multiple subsequent pixels. When scrambling, each pixel changes position with the three pixels before it according to the chaotic sequence. Finally, through many experiments, it is proved that the image encryption algorithm not only greatly improves the encryption and decryption speed, but also improves various indexes. The key space reached 2192, the average information entropy was 7.9994, the average NPCR was 99.6172, and the average UACI was 33.4646. The algorithm can also resist some common attacks and accidents, such as exhaustion attack, differential attack, noise attack, information loss and so on.

Review and experimental comparison of speckle-tracking algorithms for X-ray phase contrast imaging.

X-ray speckles have been used in a wide range of experiments, including imaging (and tomography), wavefront sensing, spatial coherence measurements, X-ray photon correlation spectroscopy and ptychography. In this review and experimental comparison, we focus on using X-ray near-field speckle grains as wavefront markers and numerical methods for retrieving the phase information they contain. We present the most common tracking methods, introducing the existing algorithms with their specifications and comparing their performances under various experimental conditions. This comparison includes applications to different types of samples: phantoms for quantitative analysis and complex samples for assessing image quality. Our goal is to unify concepts from several speckle tracking methods using consistent terminology and equation formalism, while keeping the discussion didactic and accessible to a broad audience.

Robust Watermarking Algorithm for Screen-Shooting Images Based on Pattern Complexity JND Model

Robust Watermarking Algorithm for Screen-Shooting Images Based on Pattern Complexity JND Model

Gas Leak Real-Time Detection and Volume Flow Quantification Based on Infrared Imaging and Advanced Algorithms

Gas Leak Real-Time Detection and Volume Flow Quantification Based on Infrared Imaging and Advanced Algorithms

A Compressive Super-resolution Imaging Algorithm for Multi-frequency Near-field Millimeter-wave

A Compressive Super-resolution Imaging Algorithm for Multi-frequency Near-field Millimeter-wave

Development of a low-dose strategy for propagation-based imaging helical computed tomography (PBI-HCT): high image quality and reduced radiation dose

Background. Propagation-based imaging computed tomography (PBI-CT) has been recently emerging for visualizing low-density materials due to its excellent image contrast and high resolution. Based on this, PBI-CT with a helical acquisition mode (PBI-HCT) offers superior imaging quality (e.g., fewer ring artifacts) and dose uniformity, making it ideal for biomedical imaging applications. However, the excessive radiation dose associated with high-resolution PBI-HCT may potentially harm objects or hosts being imaged, especially in live animal imaging, raising a great need to reduce radiation dose.Methods. In this study, we strategically integrated Sparse2Noise (a deep learning approach) with PBI-HCT imaging to reduce radiation dose without compromising image quality. Sparse2Noise uses paired low-dose noisy images with different photon fluxes and projection numbers for high-quality reconstruction via a convolutional neural network (CNN). Then, we examined the imaging quality and radiation dose of PBI-HCT imaging using Sparse2Noise, as compared to when Sparse2Noise was used in low-dose PBI-CT imaging (circular scanning mode). Furthermore, we conducted a comparison study on the use of Sparse2Noise versus two other state-of-the-art low-dose imaging algorithms (i.e., Noise2Noise and Noise2Inverse) for imaging low-density materials using PBI-HCT at equivalent dose levels.Results. Sparse2Noise allowed for a 90% dose reduction in PBI-HCT imaging while maintaining high image quality. As compared to PBI-CT imaging, the use of Sparse2Noise in PBI-HCT imaging shows more effective by reducing additional radiation dose (30%-36%). Furthermore, helical scanning mode also enhances the performance of existing low-dose algorithms (Noise2Noise and Noise2Inverse); nevertheless, Sparse2Noise shows significantly higher signal-to-noise ratio (SNR) value compared to Noise2Noise and Noise2Inverse at the same radiation dose level.Conclusions and significance. Our proposed low-dose imaging strategy Sparse2Noise can be effectively applied to PBI-HCT imaging technique and requires lower dose for acceptable quality imaging. This would represent a significant advance imaging for low-density materials imaging and for future live animals imaging applications.