Image Size Research Articles

Brain magnetic resonance images segmentation via improved mixtures of factor analyzers based on dynamic co-clustering

The main goal of this paper is to attain accurate and automatic detection of brain tumors in gray magnetic resonance images using reducing the local dimensions. We propose a novel model called Improved Mixtures of Factor Analyzers based on Dynamic Co-Clustering (IMFADCC) for the detection and localization of brain tumors. After image preprocessing and enhancement, the optimal numbers of row clusters and column clusters corresponding to each row cluster, alongside other model parameters, are determined concurrently based on the size of the tumor by our model. Then, using the optimal values obtained, the image is co-clustered by simultaneously clustering rows and columns until the block containing the tumor is identified. The output image is divided into a certain number of blocks, one of which contains the tumor. For post-processing, the identified block is binarized using minimum error thresholding. The proposed model accurately detects and locates the block binarized within the original image while considering the remainder of the image as background. The effectiveness of the proposed method was assessed by evaluating its accuracy, Dice, intersection on union, geometric mean, receiver operating characteristic curve, specificity, and sensitivity criteria on the BraTS2018, BraTS2019, and BraTS2020 datasets. The results show that our method has significant performance and is highly accurate in detecting tumors of different sizes in complex and high-size images. Moreover, our method outperforms the existing diagnostic methods.

Vision-based seam tracking for GMAW fillet welding based on keypoint detection deep learning model

Pre-programmed welding robots significantly improved the efficiency and quality of the welds in large batch production. In small and medium batch production, the robots need appropriate sensors to perform well and adapt to the changes and uncertainties in a noisy welding environment. Vision-based sensors enabled by machine learning are making it possible to sense in process previously not measurable. One challenge is developing artificial intelligent models capable of real-time seam tracking, particularly in fillet joints where visual analysis is hindered by non-perpendicular camera angles and arc reflections. In this paper, we propose a vision system that enables automated seam tracking with a collaborative robot. The vision-based deep learning classification model detects the tacks, where the seam is not visible. It is based on a keypoint detection deep learning model that addresses the challenges in distorted and noisy images of fillet joints between the pipes and flanges during the real-time Gas Metal Arc Welding to track the location of the seam in non tack images. The system is optimized for real time seam tracking by proposing the appropriate input image size. Multiple images and multiple points are also considered to provide a controllable signal of the location of the seam with less errors and outliers. Our proposed model can track the seam with more than 80 percent accuracy for errors less than 0.3 mm in fillet joints. The high accuracy of the proposed method would result in fewer flaws and defects and reduced rework, resulting in significant cost saving in manufacturing. The real-time monitoring also enables the adaptability to slight variations in gap.

YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

In the field of multimodal robotics, achieving comprehensive and accurate perception of the surrounding environment is a highly sought-after objective. However, current methods still have limitations in motion keypoint detection, especially in scenarios involving small target detection and complex scenes. To address these challenges, we propose an innovative approach known as YOLOv8-PoseBoost. This method introduces the Channel Attention Module (CBAM) to enhance the network’s focus on small targets, thereby increasing sensitivity to small target individuals. Additionally, we employ multiple scale detection heads, enabling the algorithm to comprehensively detect individuals of varying sizes in images. The incorporation of cross-level connectivity channels further enhances the fusion of features between shallow and deep networks, reducing the rate of missed detections for small target individuals. We also introduce a Scale Invariant Intersection over Union (SIoU) redefined bounding box regression localization loss function, which accelerates model training convergence and improves detection accuracy. Through a series of experiments, we validate YOLOv8-PoseBoost’s outstanding performance in motion keypoint detection for small targets and complex scenes. This innovative approach provides an effective solution for enhancing the perception and execution capabilities of multimodal robots. It has the potential to drive the development of multimodal robots across various application domains, holding both theoretical and practical significance.

Development of a Multi-Parametric ultrasonography nomogram for prediction of invasiveness in ductal carcinoma in situ

ObjectiveTo investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment. MethodsConsecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established. ResultsA total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867. ConclusionMulti-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization.

Optimal Re-Materialization Strategies for Heterogeneous Chains: How to Train Deep Neural Networks with Limited Memory

Training in Feed Forward Deep Neural Networks is a memory-intensive operation which is usually performed on GPUs with limited memory capacities. This may force data scientists to limit the depth of the models or the resolution of the input data if data does not fit in the GPU memory. The re-materialization technique, whose idea comes from the checkpointing strategies developed in the Automatic Differentiation literature, allows data scientists to limit the memory requirements related to the storage of intermediate data (activations), at the cost of an increase in the computational cost. This paper introduces a new strategy of re-materialization of activations that significantly reduces memory usage. It consists in selecting which activations are saved and which activations are deleted during the forward phase, and then recomputing the deleted activations when they are needed during the backward phase. We propose an original computation model that combines two types of activation savings: either only storing the layer inputs, or recording the complete history of operations that produced the outputs. This paper focuses on the fully heterogeneous case, where the computation time and the memory requirement of each layer is different. We prove that finding the optimal solution is NP-hard and that classical techniques from Automatic Differentiation literature do not apply. Moreover, the classical assumption of memory persistence of materialized activations, used to simplify the search of optimal solutions, does not hold anymore. Thus, we propose a weak memory persistence property and provide a Dynamic Program to compute the optimal sequence of computations. This algorithm is made available through the Rotor software, a PyTorch plug-in dealing with any network consisting of a sequence of layers, each of them having an arbitrarily complex structure. Through extensive experiments, we show that our implementation consistently outperforms existing re-materialization approaches for a large class of networks, image sizes and batch sizes.

Shape Recognition and Corner Points Detection in 2D Drawings Using a Machine Learning Long Short-Term Memory (LSTM) Approach

Creating a 2D geometry model from an image poses challenges for CAD users due to factors such as noise, segmentation difficulties, complex geometric structures, scale and perspective variations, and the need for CAD system compatibility. In this paper, we propose a novel deep learning approach utilizing Long-Short Term Memory (LSTM) to address these challenges. Our approach decomposes the shapes in the images into line and curve segments and accurately locates their intersection points. To enhance the model’s performance, we introduce two distinct types of features (angle and curvature features) and optimize the model through hyperparameter tuning. The resulting model exhibits robustness against noise, varying image sizes, and can effectively locate different types of intersection points. To evaluate the proposed model, we have developed a Python-based software and conducted experiments on a dataset comprising of 200 shapes with seven different resolutions. Comparative analysis against a state-of-the- art method (TCVD) from the literature demonstrates that our approach achieves higher accuracy in terms of line, curve, and intersection point detection.

Quantifying nasal deformities using a novel mathematical method to complement preoperative assessment in rhinoplasty patients

BackgroundRhinoplasty enhances facial symmetry and functionality. However, the accurate and reliable quantification of nasal defects pre-surgery remains an ongoing challenge. AimThis study introduces a novel approach for defect quantification using 2D images and artificial intelligence, providing a tool for better preoperative planning and improved surgical outcomes. Materials and MethodsA pre-trained AI model for facial landmark detection was utilised on a dataset of 250 images of male patients aged 18 to 24 who underwent rhinoplasty for cosmetic nasal deformity correction. The analysis concentrated on 36 different distances between the facial landmarks. These distances were normalised using min-max scaling to counter image size and quality variations. Post-normalisation, statistical parameters, including mean, median, and standard deviation, were calculated to identify and quantify nasal defects. ResultsThe methodology was tested and validated using images from different ethnicities and regions, showing promising potential as a beneficial surgical aid. The normalised data produced reliable quantifications of nasal defects (average 76.2%), aiding in preoperative planning and improving surgical outcomes and patient satisfaction. ApplicationsThe developed method can be extended to other facial plastic surgeries. Furthermore, it can be used to create app-based software, assist medical education, and improve patient-doctor communication. ConclusionThis novel method for defect quantification in rhinoplasty using AI and image processing holds significant potential in improving surgical planning, outcomes, and patient satisfaction, marking an essential step in the fusion of AI and plastic surgery.

Skin lesion classification based on hybrid self‐supervised pretext task

AbstractThe combination of observation of skin lesion and digital image technology contributes to the diagnosis and treatment of skin diseases. To solve the problems of large variation of target size and shape in skin disease images, small difference between disease images and normal images, and difficulty of label acquisition, we propose a classification algorithm for skin lesion based on hybrid self‐supervised pretext tasks. We combine the discriminative self‐supervised relational reasoning task with the generative self‐supervised mutual information maximization task and design the loss function. Experiments show that the generative‐discriminative self‐supervised co‐training algorithm can identify images with small differences and learn discriminative features and achieve a classification accuracy of 82.6% on the DermaMNIST dataset.

Performance of a single-use gastroscope for esophagogastroduodenoscopy: Prospective evaluation.

Background and study aims Reprocessing reusable endoscopes is challenging due to their non-sterilizable nature. Disinfection has been shown to have a significant risk of failure with serious consequences. Single-use endoscopes can eliminate contamination risk and reduce workflow delays caused by reprocessing. This study evaluated the clinical performance of single-use gastroscopes in patients undergoing esophagogastroduodenoscopy (EGD). Patients and methods In this case series, 60 patients underwent EGD using single-use gastroscopes, with 34 procedures in the endoscopy department and 26 in the intensive care unit. The primary outcome was successful completion of the intended EGD objective. Furthermore, certified endoscopists assessed device performance on a five-point Likert scale (ranging from 1-"much worse" to 5-"much better"), considering their experience with a reusable gastroscope. Results Successful completion of EGDs using only the single-use gastroscope was achieved in 58 of 60 cases (96.7%). In two cases, crossover to an ultra-slim endoscope was necessary to either reach the esophageal stenosis or to transverse the stenosis. Overall satisfaction was rated as comparable to reusable scopes in 51 of 56 cases (91.1%) and inferior in five cases (8.9%). The lower weight of the single-use gastroscope was rated as superior in 42 of 60 cases (70.0%). Drawbacks included reduced image quality (23 of 45 cases; 51.1%). Feedback included the absence of a freeze button, lens cleaning issues, and small image size. Conclusions Single-use gastroscopes exhibited a high EGD completion rate and effectiveness for various indications. Further research should focus on evaluating the implementation of single-use gastroscopes in a comprehensive context, considering clinical effectiveness, costs, and environmental impact.

Exploring patient perspectives on EQ-5D-5L data visualization within an individualized decision aid for total knee arthroplasty (TKA) in Alberta, Canada

BackgroundDecision aids can help patients set realistic expectations. In this study, we explored alternative presentations to visualise patient-reported outcomes (EQ-5D-5L) data within an online, individualized patient decision aid for total knee arthroplasty (TKA) that, in part, generates individualized comparisons based on age, sex and body mass index, to enhance usability prior to implementation into routine clinical practice.MethodsWe used data visualization techniques to modify the presentation of EQ-5D-5L outcomes data within the decision aid. The EQ-5D-5L data was divided into two parts allowing patients to compare themselves to similar individuals (1) pre-surgery and (2) 1-year post-surgery. We created 2 versions for each part and sought patient feedback on comprehension, usefulness, and visual appeal. Patients from an urban orthopedic clinic were recruited and their ratings and comments were recorded using a researcher-administered checklist. Data were managed using Microsoft Excel, R version 3.6.1 and ATLAS.ti V8 and analyzed using descriptive statistics and directed content analysis.ResultsA total of 24 and 25 patients participated in Parts 1 and 2, respectively. Overall, there was a slight preference for Version 1 in Part 1 (58.3%) and Version 2 in Part 2 (64%). Most participants demonstrated adequate comprehension for all versions (range 50–72%) and commented that the instructions were clear. While 50–60% of participants rated the content as useful, including knowing the possible outcomes of surgery, some participants found the information interesting only, were unsure how to use the information, or did not find it useful because they had already decided on a treatment. Participants rated visual appeal for all versions favorably but suggested improvements for readability, mainly larger font and image sizes and enhanced contrast between elements.ConclusionsBased on the results, we will produce an enhanced presentation of EQ-5D-5L data within the decision aid. These improvements, along with further usability testing of the entire decision aid, will be made before implementation of the decision aid in routine clinical practice. Our results on patients’ perspectives on the presentation of EQ-5D-5L data to support decision making for TKA treatments contributes to the knowledge on EQ-5D-5L applications within healthcare systems for clinical care.

Monitoring of key Camellia Oleifera phenology features using field cameras and deep learning

A rapid and accurate yield estimation is of great significance to the management and sustainable development of Camellia Oleifera forests. Consequently, the simultaneous and accurate detection of key phenology features of Camellia Oleifera (buds, flowers, fruits) is crucial for precise yield estimation. This not only enables robotic harvesting but also allows for the prediction of peak flowering and fruit ripening periods to determine the optimal harvesting time. However, in recent studies, only Camellia Oleifera fruits have been marked and detected. Therefore, to enable rapid yield estimation, it is necessary to simultaneously detect the key phenology stages (buds, flowers, fruits) of Camellia Oleifera. In this study, we annotated, trained, and predicted Camellia Oleifera buds, flowers, and fruits collected via field cameras to observe their daily quantitative changes. Quantity change curves were generated to estimate crucial phenology stages. Phenology feature detection and transfer learning were performed using the YOLO v5 model, widely used YOLO v3 model, and CenterNet model with center point prediction, all trained on the same dataset. The best model for phenology feature detection was selected based on a comparison of average precision, with the YOLO v5 model achieving a higher mean Average Precision (mAP) value of 91.31 % compared to the CenterNet (85.43 %) and YOLO v3 (81.00 %) models. In YOLO v5, the AP values for bud, flower, and fruit detection were 82.80 %, 98.13 %, and 92.99 %, respectively, surpassing the CenterNet model by 3.97 %, 2.44 %, and 11.23 %, and the YOLO v3 model by 6.39 %, 17.13 %, and 11.67 %. The image size was adapted from 4000 × 3000 pixels to 512 × 512 pixels for model optimization. Additionally, data from the Seedling Center of Liuyang City collected at different years and times were utilized to showcase the generalizability and scalability of the selected models, resulting in mAP values of 86.14 %, 80.17 %, and 69.20 % for the three above-mentioned models respectively. The plotted phenology change curves unveiled that Camellia Oleifera undergoes four stages: fruit enlargement period, bud enlargement period, flowering period, and flower wilting period. The conclusion can be drawn that using field cameras and YOLOv5 can simultaneously achieve real-time detection of key phenology features (buds, flowers, and fruits) of Camellia Oleifera, in order to further record crucial phenology patterns (such as flowering peaks and fruit ripening periods). This study offers theoretical references and scientific evidence for monitoring changes in key phenology features of Camellia Oleifera.

Detecting and counting sorghum aphid alates using smart computer vision models

Sorghum aphid [Melanaphis sorghi (Theobald)] is considered an economic pest causing significant yield losses in susceptible sorghum in the southern U.S. Infestations start with the migration of alates (i.e., winged adults) to sorghum and establishing aphid colonies. In favorable conditions, sorghum aphid can exponentially reproduce via asexual reproduction. A suggested strategy is to monitor alates to determine initial infestations and take preventive strategies, which can result in more efficient pest monitoring and management. To reduce the time of monitoring and better understand of alate establishment under field conditions, we propose using computer vision models, specifically deep learning, to detect and count alates using field-collected images. During pest monitoring, we captured 2527 images and assessed the performance of five models within the YOLOv5 architecture family using two different image sizes, including input resolutions of 640 × 640, and 1280 × 1280 pixels. We trained models to detect and count individual alates, which ranged between 1 and 100 alates/leaf. Among models, the YOLOv5l Pytorch detection model had the best overall performance at 1280 × 1280 input pixel resolution. The YOLOv5l model is a candidate model for quantifying alates on sorghum leaves using deep learning with a precision of 83.80%, 85.60% recall, and 89% mAP@0.5 with a lower mean percent error of misdetection. To enable the use of our best deep learning model by the research community, we developed a web-based application that is freely available to the public. Using this application, users can upload images to detect and count alates with a low error of misdetection.

Intelligent visually lossless compression of dental images

Background: Tendencies to increase the mean size of dental images and the number of images acquired daily makes necessary their compression for efficient storage and transferring via communication lines in telemedicine and other applications. To be a proper solution, lossy compression techniques have to provide a visually lossless option (mode) where a desired quality (invisibility of introduced distortions for preserving diagnostically valuable information) is ensured quickly and reliably simultaneously with a rather large compression ratio.Objective: Within such an approach, our goal is to give answers to several practical questions such as what encoder to use, how to set its parameter that controls compression, how to verify that we have reached our ultimate goal, what are additional advantages and drawbacks of a given coder, and so on.Methods: We analyze the performance characteristics of several encoders mainly based on discrete cosine transform for a set of 512 × 512 pixel fragments of larger size dental images produced by Morita and Dentsply Sirona imaging systems. To control the visual quality of compressed images and the invisibility of introduced distortions, we have used modern visual quality metrics and distortion invisibility thresholds established for them in previous experiments. Besides, we have also studied the so-called just noticeable distortions (JND) concept, namely, the approach based on the first JND point when the difference between an image subject to compression and its compressed version starts to appear.Results: The rate-distortion dependences and coder setting parameters obtained for the considered approaches are compared. The values of the parameters that control compression (PCC) have been determined. The ranges of the provided values of compression ratio have been estimated and compared. It is shown that the provided CR values vary from about 20 to almost 70 for modern coders and almost noise-free images that is significantly better than for JPEG. For images with visible noise, the minimal and maximal values of produced CR are smaller than for the almost noise-free images. We also present the results of the verification of compressed image quality by specialists (professional dentists).Conclusion: It is shown that it is possible and easy to carry out visually lossless compression of dental images using the proposed approaches with providing quite high compression ratios without loss of data diagnostic value.

Edge impulse-based pretrained neural network for diagnosing COVID-19

COVID-19 has wreaked havoc on a global scale, primarily owing to its extraordinary contagiousness, thereby straining local healthcare systems to their limits. While the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is known for its specificity, it suffers from time-consuming procedures and a notable false negative rate. Consequently, there is an immediate imperative for a swift and precise diagnostic approach. This paper introduces a novel concept, employing artificial intelligence, to address these challenges effectively. Specifically, this study employed a transfer learning model provided by the Edge Impulse platform and a dataset containing chest X-ray images of children. This study pre-processed these images and trained and tested them several times using different image sizes and network architectures. The experimental results show that the model achieves very high accuracy (&gt;99%) with 160*160 image size and version 1.0 or 0.35 of the network architecture. These results clearly support the hypothesis that migration learning can play an important role in the fast and accurate diagnosis of COVID-19 with appropriate image size and network architecture. This research can be used as a way to rapidly train locally adapted AI models to achieve rapid assisted diagnosis of this type of acute infectious disease.

Software implementation of the algorithm for automatic detection of lineaments and their properties on open-pit dumps

The paper presents an algorithm and a description of its software implementation for detecting lineaments (ground erosions or cracks) in aerial photography images of open-pits. The proposed approach is based on the apparatus of convolutional neural networks based on the semantic classification of binarized images of objects (lineaments), as well as graph theory for determining the geometric location of linearized objects, followed by determining their lengths and areas. Three-channel RGB images of high-resolution aerial photography (pixel 10x10 cm) were used as initial data. The software unit of the model is logically divided into three layers: pre-processing, detection and post-processing. The first level includes preprocessing of input data to form a training sample based on successive transformations of an RGB image into a binary one using the OpenCV library. A neural network of the U-Net type, which includes blocks of the convolutional (Encoder) and scanning parts (Decoder), represents the second level of the information model. At this level, automatic lineament detection (washouts) is implemented. The third level of the model is responsible for calculating the areas and lengths of lineaments. The result of the work of the convolutional neural network is transferred to the input. Lineament area is calculated by summing the total number of points multiplied by the pixel size. The length of the lineaments is computed by linearizing a plane object into a line segmental object with nodal points and then calculating the lengths between them, also relying on the resolution of the original image. The software module can work with input images, with their subsequent resulting merging to the size of the original image.

A Lightweight Model for Malicious Code Classification Based on Structural Reparameterisation and Large Convolutional Kernels

With the advancement of adversarial techniques for malicious code, malevolent attackers have propagated numerous malicious code variants through shell coding and code obfuscation. Addressing the current issues of insufficient accuracy and efficiency in malicious code classification methods based on deep learning, this paper introduces a detection strategy for malicious code, uniting Convolutional Neural Networks (CNNs) and Transformers. This approach utilizes deep neural architecture, incorporating a novel fusion module to reparametrize the structure, which mitigates memory access costs by eliminating residual connections within the network. Simultaneously, overparametrization during linear training time and significant kernel convolution techniques are employed to enhance network precision. In the data preprocessing stage, a pixel-based image size normalization algorithm and data augmentation techniques are utilized to remedy the loss of texture information in the malicious code image scaling process and class imbalance in the dataset, thereby enhancing essential feature expression and alleviating model overfitting. Empirical evidence substantiates this method has improved accuracy and the most recent malicious code detection technologies.

A Hybrid Cryptosystem Incorporating a New Algorithm for Improved Entropy.

Today, safeguarding sensitive content through encryption is crucial. This work presents a hybrid cryptosystem for images that employs both asymmetric and symmetric encryption. The asymmetric component involves applying the Diffie-Hellman protocol and the ElGamal cryptosystem to securely transmit two constants. These constants are necessary for the symmetrical aspect to generate dynamic permutations, substitution boxes, and round keys. Following an encryption process with fourteen rounds, the encrypted images are processed by an algorithm proposed to enhance entropy, a critical metric for assessing encryption quality. It increases the frequencies of the basic colors to achieve a histogram closely resembling a uniform distribution, but it increases the image size by approximately 8%. This improves the entropy values achieved by the hybrid cryptosystem, bringing them remarkably close to the ideal value of 8.0. In specific instances, the entropy values were elevated from 7.99926 to 8.0. The proposed method exhibits resilience against various attacks, including differential, linear, brute force, and algebraic attacks, as evaluated through the entropy, correlation, goodness of fit, Discrete Fourier Transform (DFT), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Avalanche Criteria (AC), contrast, energy, and homogeneity. Further, encrypted images are subjected to noise attacks ranging from 20% to 50% noise, including additive, multiplicative, occlusion noise, as well as the newly introduced χ2 noise. The noise damage is quantified using the proposed Similarity Parameter (SP), and a 3 × 3 median filter is employed to enhance the visual quality.

Quantum annealing-based computed tomography using variational approach for a real-number image reconstruction

Objective. Despite recent advancements in quantum computing, the limited number of available qubits has hindered progress in CT reconstruction. This study investigates the feasibility of utilizing quantum annealing-based computed tomography (QACT) with current quantum bit levels. Approach. The QACT algorithm aims to precisely solve quadratic unconstrained binary optimization problems. Furthermore, a novel approach is proposed to reconstruct images by approximating real numbers using the variational method. This approach allows for accurate CT image reconstruction using a small number of qubits. The study examines the impact of projection data quantity and noise on various image sizes ranging from 4 × 4 to 24 × 24 pixels. The reconstructed results are compared against conventional reconstruction algorithms, namely maximum likelihood expectation maximization (MLEM) and filtered back projection (FBP). Main result. By employing the variational approach and utilizing two qubits for each pixel of the image, accurate reconstruction was achieved with an adequate number of projections. Under conditions of abundant projections and lower noise levels, the image quality in QACT algorithm outperformed that of MLEM and FBP algorithms. However, in situations with limited projection data and in the presence of noise, the image quality in QACT was inferior to that in MLEM. Significance. This study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction. Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction.

A novel technique for image steganography based on maximum energy seam

Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represents the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stego-image, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of images and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1.

CROSS STYLE IMAGE SYNTHESIS THROUGH SEMI COUPLED DICTIONARY LEARNING AND GENERALIZED CROSS VALIDATION ALGORITHM

In various Computer vision applications for better visualization, interpretation and better recognition, we frequently want to change an image in one style into another one which is a large research area of compressive sensing. Algorithms on compressed sensing are in demand to create better reconstruction of the images while taking less computational time and requiring less storage capacity. In the present work attempts are put in the same direction and brought an algorithm which includes semi coupled dictionary learning (SCDL) model of compressed sensing with Singular Value Decomposition (SVD) based Generalized Cross Validation (GCV) algorithm. Using this algorithm one can find the different value of regularization parameters for different sizes of images and that helps to get better reconstruction of the images with ease while doing experiments on cross style image synthesis problems.