Image Area Research Articles

Research on detection method of photovoltaic cell surface dirt based on image processing technology

In view of the reduced power generation efficiency caused by ash or dirt on the surface of photovoltaic panels, and the problems of heavy workload and low efficiency faced by manual detection, this study proposes a method to detect dust or dust on the surface of photovoltaic cells with the help of image processing technology to timely eliminate hidden dangers and improve power generation efficiency.This paper introduces image processing methods based on mathematical morphology, such as image enhancement, image sharpening, image filtering and image closing operation, which makes the image better highlight the target to be recognized. At the same time, it also solves the problem of uneven image binarization caused by uneven illumination in the process of image acquisition. By using the image histogram equalization, the gray level concentration area of the original image is opened or the gray level is evenly distributed, so that the dynamic range of the pixel gray level is increased, so that the image contrast or contrast is increased, the image details are clear, to achieve the purpose of enhancement. When identifying the target area, the method of calculating the proportion of the dirt area to the whole image area is adopted, and the ratio exceeding a certain threshold is judged as a fault. In addition, the improved A* path planning algorithm is adopted in this study, which greatly improves the efficiency of the unmanned aerial vehicle detection of photovoltaic cell dirt, saves time and resources, reduces operation and maintenance costs, and improves the operation and maintenance level of photovoltaic units.

Analysis of skin health management through telemedicine and mobile health in dermatology in the post-COVID era

Mobile health has made significant strides in the field of tele-dermatology (TD) following the developments post the COVID-19 pandemic. The application of telemedicine and mobile health to dermatology holds the potential to enhance the quality of healthcare for citizens and streamline workflows in the healthcare domain, hence this subject is of great importance. This research encompasses the last three years. A comprehensive overview examining the opportunities, perspectives, and encountered challenges regarding the integration of TD with mHealth has been provided. The narrative review methodology is based on (I) conducting research via PubMed and Scopus, and (II) conducting compliance assessment using the proposed parameters. The outcome of the research indicates rapid progress in the integration of TD with mobile health during the COVID-19 pandemic. This integration has facilitated the monitoring of dermatological issues and allowed for remote specialist visits, thereby reducing face-to-face interactions. Artificial intelligence and mobile applications have empowered citizens to take a more active role in their own healthcare, which is distinct from other imaging areas where information exchange is limited only to professionals. Opportunities that TD can offer in the field of mobile health include enhancing service quality, increasing the efficiency of healthcare processes, reducing costs, and providing more accessible care. This is applicable not only to conditions like acne, vitiligo, psoriasis, and skin cancers but also to other conditions. Integration with artificial intelligence and augmented reality (AR), along with the use of wearable sensors, are expected as future developments. However, the integration of TD with mobile health brings along issues and challenges related to regulation, ethics, cybersecurity, data privacy, and device management. These issues, along with the involvement of citizens in the process, should be addressed by scientists and policymakers.

A two-stage image enhancement and dynamic feature aggregation framework for gastroscopy image segmentation

Accurate and reliable automatic segmentation of lesion areas in gastroscopy images can assist endoscopists in making diagnoses and reduce the possibility of missed or incorrect diagnoses. This paper presents a two-stage framework for segmenting gastroscopy images, which aims to improve the accuracy of medical image segmentation tasks using limited datasets. The proposed framework consists of two stages: the Image Enhancement Stage and the Lesion Segmentation Stage. First, in the Image Enhancement Stage, an image enhancement solution called TDC-Enhance is proposed to enrich the original small-scale gastroscopy image dataset. This solution performs Texture Enhancement, Detail Enhancement, and Color Enhancement on the original images. Then, in the Lesion Segmentation Stage, a multi-path automatic segmentation network for gastroscopy images, named DynaSiam, is introduced. DynaSiam comprises a Dependent Encoder, a Shared Encoder, and a Fusion Decoder. It learns feature information related to the lesion region by encoding the different enhanced images obtained in the Image Enhancement Stage as inputs to the multi-path network. Additionally, a Dynamic Feature Interaction (DFI) block is designed to capture and learn deeper image information, thereby improving the segmentation performance of the model. The experimental results show that the proposed method achieves a 90.80% mIoU, 92.71% Dice coefficient and 96.31% Accuracy. Other performance metrics also indicate the best performance, suggesting that the proposed model has significant potential for clinical analysis and diagnosis. Code and implementation details can be found on GitHub: https://github.com/kyasulee/DynaSiam.

Soft X-ray performance of an optimised charge-coupled device for astronomy

For the upcoming European Space agency (ESA)/Chinese Academy of Sciences (CAS) SMILE mission, launching in 2025, large-format soft X-ray optimised CCDs have been manufactured by Teledyne e2v, named the CCD370. The CCDs are approximately 8 cm × 8 cm and are comprised of 4510 × 4510 18 μm pitch pixels, with a store shield covering approximately 1/7th of the active imaging area to facilitate frame-transfer operation mode. To optimise quantum efficiency within the soft X-ray energy band, the device is 16 μm thick, back illuminated, and has an additional back surface passivation process. The focal plane of the soft X-ray imager (SXI) on the SMILE spacecraft will be comprised of 2 CCD370s, operating with 6 × 6 on-chip binning to mitigate against CTI-induced charge transfer losses and charge spreading throughout the 3-year mission lifetime.As part of the pre-flight testing and calibration, a CCD370 was characterised at the PTB beamline at the BESSY 2 synchrotron in Berlin, and key metrics such as quantum efficiency and energy resolution in the 0.2–1.8 keV energy band were assessed. The quantum efficiency measurements show expected performance within specification for the instrument, and also match a transmission-layer QE model. The energy resolution is 68 ± 2 eV FWHM @ 1.2 keV, which is an improvement over the current generation of X-ray telescopes such as XMM-Newton, Chandra, and the Swift X-ray Telescope. Although competing technologies such as DEPFETs and scientific CMOS image sensors now have similar performance to CCDs, the performance shown here can still easily satisfy requirements for novel X-ray instruments, and can still be useful in future astronomy missions given the rich heritage, high technology-readiness-level, and maturity of the technology.

Multi-scale occlusion suppression network for occluded person re-identification

In practical application scenarios, the occlusion caused by various obstacles greatly undermines the accuracy of person re-identification. Most existing methods for occluded person re-identification focus on inferring visible parts of the body through auxiliary models, resulting in inaccurate feature matching of parts and ignoring the problem of insufficient occluded samples, which seriously affects the accuracy of occluded person re-identification. To address the above issues, we propose a multi-scale occlusion suppression network (MSOSNet) for occluded person re-identification. Specifically, we first propose a dual occlusion augmentation module (DOAM), which combines random occlusion with our proposed novel cross occlusion to generate more diverse occlusion data. Meanwhile, we design a novel occluded-aware spatial attention module (OSAM) to enable the network to focus on non-occluded areas of pedestrian images and effectively extract discriminative features. Ultimately, we propose a part feature matching module (PFMM) that utilizes graph matching algorithms to match non-occluded body parts of pedestrians. Extensive experimental results on both occluded and holistic datasets validate the effectiveness of our method.

A Novel Adversarial Deep Learning Method for Substation Defect Image Generation.

The presence of defects in substation equipment is a major factor affecting the safety of power transmission. Therefore, timely and accurate detection of these defects is crucial. As intelligent inspection robots advance, using mainstream object detection models to diagnose surface defects in substation equipment has become a focal point of current research. However, the lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. To address the issue of insufficient training data for defect images with complex backgrounds, such as rust and surface oil leakage in substation equipment, which leads to the poor performance of detection models, this paper proposes a novel adversarial deep learning model for substation defect image generation: the Abnormal Defect Detection Generative Adversarial Network (ADD-GAN). Unlike existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model uses a joint discriminator for both overall images and defect images to address the issue of low attention to local defect areas, thereby reducing the loss of image features. This approach enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves a mean average precision (mAP) of 81.5% on the test dataset, and outperforms other image data augmentation and generation methods. This confirms that the ADD-GAN method can generate a high-fidelity image dataset of substation equipment defects.

Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM

Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.

An Infrared Aircraft Detection Algorithm Based on Context Perception Feature Enhancement

To address the issue of insufficient extraction of target features and the resulting impact on detection performance in long-range infrared aircraft target detection caused by small imaging area and weak radiation intensity starting from the idea of perceiving target context to enhance the features extracted by convolutional neural network, this paper proposes a detecting algorithm based on AWFGLC (adaptive weighted fusion of global–local context). Based on the mechanism of AWFGLC, the input feature map is randomly reorganized and partitioned along the channel dimension, resulting in two feature maps. One feature map is utilized by self-attention for global context modeling, establishing the correlation between target features and background features to highlight the salient features of the target, thereby enabling the detecting algorithm to better perceive the global features of the target. The other feature map is subjected to window partitioning, with max pooling and average pooling performed within each window to highlight the local features of the target. Subsequently, self-attention is applied to the pooled feature map for local context modeling, establishing the correlation between the target and its surrounding neighborhood, further enhancing the weaker parts of the target features, and enabling the detecting algorithm to better perceive the local features of the target. Based on the characteristics of the target, an adaptive weighted fusion strategy with learnable parameters is employed to aggregate the global context and local context feature maps. This results in a feature map containing more complete target information, enhancing the ability of the detection algorithm to distinguish between target and background. Finally, this paper integrates the mechanism of AWFGLC into YOLOv7 for the detection of infrared aircraft targets. The experiments indicate that the proposed algorithm achieves mAP50 scores of 97.8% and 88.7% on self-made and publicly available infrared aircraft datasets, respectively. Moreover, the mAP50:95 scores reach 65.7% and 61.2%, respectively. These results outperform those of classical target detection algorithms, indicating the effective realization of infrared aircraft target detection.

Systematic Analysis of Deep Learning Models vs. Machine Learning

Deep learning (DL) and classical machine learning (ML) models are compared and contrasted in this study, which offers a complete overview of the differences and technological improvements between the two types of models. Through an analysis of a diverse range of research publications, the study draws attention to the distinct advantages and uses of both techniques. Deep learning, which is characterized by its use of neural networks with several layers, is particularly effective at managing massive datasets that are not organized. It has also made great progress in the areas of image and audio recognition, natural language processing, and complicated pattern identification exercises. On the other hand, classic machine learning models, which are based on the extraction of features and simpler methods, continue to be quite successful in structured data situations such as classification, regression, and clustering challenges. By concentrating on aspects such as data quantity, computing resources, and unique application needs, the survey sheds light on the parameters that should be considered when selecting between deep learning and machine learning. In addition to this, it addresses the ever-changing environment of hybrid models, which combine methods from both deep learning and machine learning in order to capitalize on the advantages of both approaches. This study highlights the significance of contextual awareness in the fast-developing area of artificial intelligence by providing researchers and practitioners with useful insights that can be used to deploy the AI models that are the most appropriate for their particular requirements.

Differential confocal effective measurement region extraction method based on dual measurement

Abstract Aiming at the problem that the existing differential confocal measurement (DCM) method lacks the ability to decouple the differential signals from over-range the depth data, which in turn leads to measurement errors, this study proposes a differential confocal effective measurement region extraction method based on dual measurement (D-DCM). The first measurement is a double blur depth measurement, which is a coarse measurement, and the initial acquisition of the depth map is realized by applying a known Gaussian kernel blurring to the focal plane image and calculating the edge gradient ratios between the original image and the blurred image. This depth map is used to determine whether the depth of the measured sample in the field of view is within the differential confocal range and to segment it. The second measurement is a DCM, which is a fine measurement. The effective measurement area of the segmented image is second measured with high precision depth measurement. It is shown by single and multiple step heights experiments that the proposed D-DCM method can effectively solve the problem of decoupling the differential signal from the axial depth in differential confocal.

Using Thermal Signature to Evaluate Heat Stress Levels in Laying Hens with a Machine-Learning-Based Classifier.

Infrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens' bodies were cut out. Rectal temperature was used to label each infrared thermography data as "Danger" or "Normal", and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.

Contraband detection method in X-ray image based on improved CenterNet network

Security inspection has been played a crucial role in ensuring the safety of railway passenger transportation. The detection of contraband in X-ray images is an important part of safety inspection work. Since the X-ray image of security inspection is a pseudo color image with a unique imaging style, and the restricted objects are mostly small targets, the accuracy of the object detection algorithm using visible light images directly is not high. This research will propose an improved CenterNet method, and design a 16 times down sampling rate backbone network, which is more suitable for detecting contraband in X-ray images. This article will introduce attention mechanisms to increase attention to effective and key areas in X-ray images, and reveal the mechanisms of spatial and channel attention in X-ray images. Furthermore, by designing an adaptive feature fusion mechanism, the drawback of the original CenterNet by using only the last layer of output features is compensated. Experiments have shown that on the SIXray dataset, mAP is improved by 3.3% compared to the original CenterNet method, and 5.5%, 2.4%, 3.6%, 10.1% compared to SSD, Yolo V3, FPN, DERT algorithms. The detection accuracy of restricted items in X-ray images is effectively improved.

Autonomous navigation method based on RGB‐D camera for a crop phenotyping robot

AbstractPhenotyping robots have the potential to obtain crop phenotypic traits on a large scale with high throughput. Autonomous navigation technology for phenotyping robots can significantly improve the efficiency of phenotypic traits collection. This study developed an autonomous navigation method utilizing an RGB‐D camera, specifically designed for phenotyping robots in field environments. The PP‐LiteSeg semantic segmentation model was employed due to its real‐time and accurate segmentation capabilities, enabling the distinction of crop areas in images captured by the RGB‐D camera. Navigation feature points were extracted from these segmented areas, with their three‐dimensional coordinates determined from pixel and depth information, facilitating the computation of angle deviation (α) and lateral deviation (d). Fuzzy controllers were designed with α and d as inputs for real‐time deviation correction during the walking of phenotyping robot. Additionally, the method includes end‐of‐row recognition and row spacing calculation, based on both visible and depth data, enabling automatic turning and row transition. The experimental results showed that the adopted PP‐LiteSeg semantic segmentation model had a testing accuracy of 95.379% and a mean intersection over union of 90.615%. The robot's navigation demonstrated an average walking deviation of 1.33 cm, with a maximum of 3.82 cm. Additionally, the average error in row spacing measurement was 2.71 cm, while the success rate of row transition at the end of the row was 100%. These findings indicate that the proposed method provides effective support for the autonomous operation of phenotyping robots.

Boundaries, Structure, and Integrity of the Occupation Layer at the Gordinskoye I (Guryakar) Fortified Settlement, Udmurtia (9th–13th Centuries), According to Multispectral Imaging

Science-based studies at Gordinskoye I (Guryakar) fortified settlement in northern Udmurtia (9th–13th centuries AD) were carried out. The occupation layer had been largely destroyed by plowing. The statistical analysis of multispectral images and segmentation of the generated images of vegetation distribution make it possible to assess the integrity of the occupation layer. The preliminary conclusion about the presence of its parts, varying in thickness, is based on the analysis of the configuration of areas of segmented multispectral images and the correspondence of diverse segments to relief features. Assessment of archaeological context and of the preservation of occupation layer (superficially disrupted, replaced, or transported) is possible only with the use of geophysical and soil studies and targeted excavations. The available reference data allow us to interpret individual segments present solely on the flat surface of the promontory. The assessment of the parameters of the occupation layer within the entire survey area is possible through the extrapolation of properties of vegetation segments with known characteristics. Based on the totality of data, it can be assumed that the built-up area of Guryakar was limited by the outer fortification line. A thick occupation layer is localized on the promontory, within two inner structural parts of the site. Two other parts of Guryakar, situated between the residence area and the outer fortification line, were probably used for utility purposes and manufacture. The deposition of the medieval occupation layer was less intense there. Additional markers of the fortifications, delimiting the boundaries of the settlement’s structural parts, are vertically oriented linear areas of the transported layer on slopes of the promontory.

Clustering-based interactive image segmentation

Objectives. The task of color image segmentation without the use of preliminary training is considered. It arises, for example, when it is necessary to perform image segmentation with semantic and color properties unknown in advance immediately after their acquisition, or when the set of images intended for segmentation is too small, as well as when performing preliminary "exploratory" analysis of images. In such cases, powerful neural network and other segmentation tools that require deep learning can not be used.Methods. An algorithm for interactive image segmentation is proposed, based on the analysis of the colors of areas selected interactively. First, in interactive mode, the image areas belonging to the objects are selected very approximately, and then regions belonging to the background are chosen. In the next step, the set of colors of the selected object areas and the set of colors of the selected background areas are clustered separately by one of the clustering algorithms, for example, k-means, fuzzy c-means, or the multi-level clustering algorithm proposed by the author. After this, non-informative elements are removed from the set of cluster centers describing the objects and the set of clusters presenting the background. The modified sets of object and background cluster centers are used for image segmentation.Results . The constructed algorithm allows selection of the required objects in color images if the colors of the objects and the background are different. Interactive selection of object areas and background areas does not require accuracy or much effort and usually takes several tens of seconds. For selection, rectangular areas that lie entirely inside the object images, and rectangular areas that belong completely to the background can be used. Below an example of interactive regions selection and color image segmentation is shown.Conclusion. The experiments performed showed the effectiveness of the proposed approach to segmenting color images. It can be used in cases where the semantic and color properties of images are not known in advance, and in cases where the use of more powerful deep learning methods, including neural networks, is too expensive or impossible.

Risk factors for preoperative neurological impairment in patients with spinal meningioma: A retrospective multicenter study

BackgroundPatients with spinal meningioma may present preoperatively with paralysis and sensory deficits. However, there is a paucity of detailed evaluations and a lack of consensus regarding imaging findings that are predictive of neurological symptoms in patients with spinal meningioma. MethodsHerein, a total of 55 patients who underwent surgical resection of spinal meningiomas in eight hospitals between 2011 and 2021 were enrolled. Patient characteristics, degree of muscle weakness, sensory disturbances, and the presence of bowel/bladder dysfunction (BBD) before surgical treatment were evaluated using medical records. Patients with American Spinal Injury Impairment Scale grades A-C and the presence of BBD were classified into the paralysis (+) group. Patients with sensory disturbances were assigned to the sensory disturbance (+) group. Based on magnetic resonance (MR) and computed tomography images, the tumor location was classified according to the spinal level and its attachment to the dura mater. To evaluate tumor size, the tumor occupation ratio (OR) was calculated using the area and distance measurement method in horizontal MR images, and the maximum length and area of the tumor in the sagittal plane were measured. ResultsOf all patients, 85 % were women. The mean age of patients at surgery was 69.7 years. Twenty-eight (51 %) and 41 (75 %) patients were classified into the paralysis (+) and sensory disturbance (+) groups, respectively. The average tumor length and area in the sagittal plane were 19.6 mm and 203 mm2, respectively; OR-area and diameters were 70.3 % and 72.3 %, respectively. In univariate analyses, tumor length and area in the sagittal plane were significant risk factors for paralysis. OR-diameter, symptom duration, and a low MIB-1 index correlated with sensory disturbances. Multivariate logistic regression analysis demonstrated that the area and length of the tumor in the sagittal plane were significantly correlated with paralysis, whereas the OR-diameter and symptom duration significantly correlated with sensory disturbances. The cut-off values for the area and length of the tumor in the sagittal plane to predict paralysis were 243 mm2 and 20.1 mm, respectively. ConclusionsPreoperative paralysis in patients with spinal meningiomas was significantly associated with sagittal tumor size than with high tumor occupancy in the horizontal plane. Sensory disturbances were associated with high occupancy in the horizontal plane. Patients with spinal meningiomas > 20 mm in length or 243 mm2 in area in the sagittal plane are at risk of developing paralysis and could be considered for surgery even in the absence of paralysis.

Clinical issues and challenges in imaging of gastrointestinal diseases: A minireview and our experience.

Imaging techniques play a crucial role in the modern era of medicine, particularly in gastroenterology. Nowadays, various non-invasive and invasive imaging modalities are being routinely employed to evaluate different gastrointestinal (GI) diseases. However, many instrumental as well as clinical issues are arising in the area of modern GI imaging. This minireview article aims to briefly overview the clinical issues and challenges encountered in imaging GI diseases while highlighting our experience in the field. We also summarize the advances in clinically available diagnostic methods for evaluating different diseases of the GI tract and demonstrate our experience in the area. In conclusion, almost all imaging techniques used in imaging GI diseases can also raise many challenges that necessitate careful consideration and profound expertise in this field.

Тестування методу топографічної спорідненості зображень на зображеннях поверхні землі

Introduction. In connection with the development of the method of topographic affinity of images, it became necessary to conduct its testing according to the criteria of workability and efficiency. The purpose of the paper is testing of the method of determining the topographic affinity of images based on taking into account the detected special zones on the images of the natural landscape for the autonomous navigation of UAVs. Results. According to the results of testing for three tasks, the method showed its effectiveness at the level of 100%. Conclusions. The method of topographic affinity of images can work with a large number of complex and diverse images of the Earth's surface, which cannot be analyzed by other known methods, and with high efficiency. It can be used to build a system of autonomous navigation of UAVs separately or together with other methods. Keywords: unmanned aerial vehicle, unmanned aircraft complex, autonomous navigation, special points, special areas, method of analysis of special areas of images.

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

The exponential surge in specialized image editing software has intensified visual forgery, with splicing attacks emerging as a popular forgery technique. In this context, Siamese neural networks are a remarkable tool in pattern identification for detecting image manipulations. This paper introduces a deep learning approach for splicing detection based on a Siamese neural network tailored to identifying manipulated image regions. The Siamese neural network learns unique features of specific image areas and detects tampered regions through feature comparison. This architecture employs two identical branches with shared weights and image features to compare image blocks and identify tampered areas. Subsequently, a K-means algorithm is applied to identify similar centroids and determine the precise localization of duplicated regions in the image. The experimental results encompass various splicing attacks to assess effectiveness, demonstrating a high accuracy of 98.6% and a precision of 97.5% for splicing manipulation detection. This study presents an advanced splicing image forgery detection and localization algorithm, showcasing its efficacy through comprehensive experiments.

A randomized control study on post-operative iris distortion following small-pupil cataract surgery using B-HEX pupil expander versus Malyugin ring

BackgroundThe aim of the study was to evaluate postoperative pupil distortion following small pupil cataract surgeries performed using B-HEX and Malyugin rings (MR). MethodsA randomized control trial was conducted from June 2020 to June 2023 at a tertiary eye-care hospital. The study consisted of 64 participants for cataract surgery with small pupil. There were two groups, one undergoing surgery with the use of B-HEX pupil expander and other with MR intraoperatively and the rest of the surgery was proceeded as per the convention. Areas of preoperative and postoperative images was calculated, put into an online software and pupil distortion was calculated in percentage. Two-tailed t-test was used to see the difference between the two groups. ResultsMean age at presentation was 70.5 ± 10.12 years. Most common cause for small pupil was tamsulosin therapy. Mean size of small-pupil was 3.0 ± 1.1 mm. With the application of two rings, mean pupillary area preoperatively was 4178.23 ± 1589.46 and postoperatively was 6100.44 ± 2658.28 following the use of MR, respectively and 30,002.93 ± 13,193.40 preoperatively and 37,648.26 ± 15,207.01 postoperatively following the use of B-Hex ring respectively. Comparing baseline area from pupillary area at 1-month follow-up, a significant increase was noted for both the rings. Also, MR caused significantly more pupillary distortion compared to B-HEX ring (p < 0.05). ConclusionMR causes significantly more pupillary distortion in the postoperative period compared to B-HEX ring. Though, both the rings cause pupillary distortion, these devices expand the surgical area adequately, ease the procedure, decrease risk of complications achieving good functional visual outcomes.