Image Size Research Articles

Double refractive particle tracking and sizing

We present a novel bifocal imaging method enabling three-dimensional particle tracking and size determination employing a single camera only. The method is based on double refraction causing a particle to be imaged twice, each particle image of different blur. From these double images, a linear calibration function can be derived allowing to determine the three-dimensional particle position unambiguously over the entire depth of measurement volume. As this calibration function is independent of the particle size used, the particle size can be determined simultaneously by relating size of the double images and depth position of the particle. To prove the applicability, a co-laminar flow of two particle suspensions with particles of 1.14 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m and 2.47 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m in diameter was measured in a Y-shaped microchannel. While the laminar flow field was measured with very low uncertainty and independent of the particle size, the particle size distributions determined reproduced reliably the size distributions expected for the co-laminar flow applied, with a precision of about 98.6 %\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} regarding the particle size discrimination. The progress for research is a new method readily to implement in common optical setups, promising, for example, valuable insights in polydisperse suspension flows—the vast majority of flows in fundamental research and applications.Graphical abstract

Penggunaan Media Gambar dalam Mengatasi Kesulitan Membaca Siswa Kelas II di SD Swasta Islam Nursyamsiani Desa Bintang Meriah Kecamatan Batang Kuis

This research is entitled "The Use of Image Media in Overcoming Reading Difficulties for Grade II Students at Nursyamsiani Islamic Private Elementary School, Bintang Meriah Village, Batang Kuis District." The objectives are to: 1) Identify the forms of reading difficulties of grade II students; 2) Knowing the effectiveness of image media in overcoming these difficulties; 3) Identify obstacles in the use of image media; and 4) Reveal teachers' efforts in overcoming these obstacles. This research is qualitative-descriptive, involving the principal and homeroom teacher as subjects. Data collection is carried out through observation, interviews, and data analysis. The results of the study showed: 1) Reading difficulties include recognition of similar letters and difficulty in prociting a combination of letters; 2) Image media helps overcome reading difficulties; 3) Constraints include image size, limited facilities, and funds; 4) Teachers' efforts include providing opportunities to see pictures in turns and re-explain the material. This result is expected to encourage an increase in teachers' creativity in the use of learning media to increase interest and effectiveness in learning.

Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour

This study addresses computer-aided breast cancer diagnosis through a hybrid framework for breast tumor segmentation in ultrasound images. The core of the three-stage method is based on the autoencoder convolutional neural network. In the first stage, we prepare a hybrid pseudo-color image through multiple instances of fuzzy connectedness analysis with a novel distance-adapted fuzzy affinity. We produce different weight combinations to determine connectivity maps driven by particular image specifics. After the hybrid image is processed by the deep network, we adjust the segmentation outcome with the Chan-Vese active contour model. We find the idea of incorporating fuzzy connectedness into the input data preparation for deep-learning image analysis our main contribution to the study. The method is trained and validated using a combined dataset of 993 breast ultrasound images from three public collections frequently used in recent studies on breast tumor segmentation. The experiments address essential settings and hyperparameters of the method, e.g., the network architecture, input image size, and active contour setup. The tumor segmentation reaches a median Dice index of 0.86 (mean at 0.79) over the combined database. We refer our results to the most recent state-of-the-art from 2022–2023 using the same datasets, finding our model comparable in segmentation performance.

Research on X-ray security contraband identification technology based on lightweight YOLOv8

Aiming at the difficulty of identification and localization in the detection of contraband targets in X-ray images, as well as the demand for upgrading and deployment of security equipment during the peak flow stage, a lightweight X-ray security contraband detection algorithm is proposed and optimized for YOLOv8. Firstly, the number of channels in the original network is reduced according to a predetermined ratio, which effectively reduces model parameters and computational complexity and achieves better results than the current mainstream lightweight methods, such as model pruning and detection backbone optimization. Secondly, due to the presence of objects of various scales and sizes in X-ray security inspection images, we introduced the BiFPN feature extraction strategy in the neck. We designed a simplified version of BiFPN based on the YOLOv8 network structure to effectively integrate feature information from different levels and scales. Finally, we incorporated the idea of Focal Loss to address the issue of imbalance between positive and negative samples and proposed a new regression loss function, Focal-GIoU Loss, which combines GIoU Loss. This increases the loss weight for important but difficult-to-detect samples, such as small targets and overlapping targets, making the model more focused on these critical samples. Results show that our proposed YOLO-CBF improves mAP by 1.5%, 0.9%, and 0.5% on three datasets with different levels of occlusion in PIDray, while reducing the parameter count and computational cost from the original 3.0M and 8.1G to 2.1M and 6.3G.Moreover, it performs well in comparative experiments between different models and in generalization experiments across different datasets.

Dadnet: dual-attention detection network for crack segmentation on tomb murals

Many tomb murals have punctate losses, cracks, and craquelure due to underground subsidence and changes in their physical support. Visual non-destructive detection techniques enable rapid assessment of how much tomb murals are affected by cracking, providing suggestions for their conservation. However, tomb murals are typically created by sketching outlines and then colored. Detailed sketches can easily interfere with crack detection work, requiring the use of deep learning network to better learn crack features and improve detection accuracy. At the same time the limited data of tomb mural presents a challenge to build a deep learning network. To address these issues, this paper introduces a novel dual-attention detection network (DADNet) for crack segmentation of tomb murals. In this work, a customized dataset is first constructed by collecting mural images from the Tang Dynasty tombs. Then the ConvNeXt framework serves as the basis for feature extraction, enhancing the process. Lastly, a dual-attention module utilizing neighborhood attention and biaxial attention is employed to accurately identify the crack regions. Neighborhood attention performs a local self-attention operation around the pixel point, addressing the limitations of self-attention. This approach significantly reduces computational demands as the image size increases. Biaxial attention performs attention calculations in the horizontal and vertical directions. This compensates for the limitation of neighborhood attention in capturing global dependencies. Our DADNet outperformed the competing methods, achieving the highest recorded scores of 78.95% for MIoU and 61.05% for the Jaccard index.

Multi exposure fusion for high dynamic range imaging via multi-channel gradient tensor

Multi-exposure fusion (MEF) is an effective technique for directly fusing a sequence of low dynamic range (LDR) images from a high dynamic range (HDR) natural scene. The goal is to generate an information enriched LDR image. Despite its effectiveness, current MEF methods often encounter issues such as detail loss and color degradation. Additionally, existing algorithms often struggle to balance image quality and computation time, particularly for large-sized images. This paper introduces an innovative MEF algorithm that address these challenges, offering improved performance and computational time across all image sizes. The algorithm employs a multi-channel gradient tensor on RGB images to effectively capture the contrast information among the three channels. This mechanism allows an edge-preserving image filter to maintain edges while smoothing weight maps. To enhance computational efficiency, the algorithm uses a fast approximation method suitable for large sized images. Our comprehensive experimental results demonstrate that the proposed method outperforms existing MEF techniques both quantitatively and qualitatively. Furthermore, our method reduces computational time by approximately 30% compared to the most recent state-of-the-art techniques.

LesionNet: an automated approach for skin lesion classification using SIFT features with customized convolutional neural network.

Accurate detection of skin lesions through computer-aided diagnosis has emerged as a critical advancement in dermatology, addressing the inefficiencies and errors inherent in manual visual analysis. Despite the promise of automated diagnostic approaches, challenges such as image size variability, hair artifacts, color inconsistencies, ruler markers, low contrast, lesion dimension differences, and gel bubbles must be overcome. Researchers have made significant strides in binary classification problems, particularly in distinguishing melanocytic lesions from normal skin conditions. Leveraging the "MNIST HAM10000" dataset from the International Skin Image Collaboration, this study integrates Scale-Invariant Feature Transform (SIFT) features with a custom convolutional neural network model called LesionNet. The experimental results reveal the model's robustness, achieving an impressive accuracy of 99.28%. This high accuracy underscores the effectiveness of combining feature extraction techniques with advanced neural network models in enhancing the precision of skin lesion detection.

Mamba- and ResNet-Based Dual-Branch Network for Ultrasound Thyroid Nodule Segmentation.

Accurate segmentation of thyroid nodules in ultrasound images is crucial for the diagnosis of thyroid cancer and preoperative planning. However, the segmentation of thyroid nodules is challenging due to their irregular shape, blurred boundary, and uneven echo texture. To address these challenges, a novel Mamba- and ResNet-based dual-branch network (MRDB) is proposed. Specifically, the visual state space block (VSSB) from Mamba and ResNet-34 are utilized to construct a dual encoder for extracting global semantics and local details, and establishing multi-dimensional feature connections. Meanwhile, an upsampling-convolution strategy is employed in the left decoder focusing on image size and detail reconstruction. A convolution-upsampling strategy is used in the right decoder to emphasize gradual feature refinement and recovery. To facilitate the interaction between local details and global context within the encoder and decoder, cross-skip connection is introduced. Additionally, a novel hybrid loss function is proposed to improve the boundary segmentation performance of thyroid nodules. Experimental results show that MRDB outperforms the state-of-the-art approaches with DSC of 90.02% and 80.6% on two public thyroid nodule datasets, TN3K and TNUI-2021, respectively. Furthermore, experiments on a third external dataset, DDTI, demonstrate that our method improves the DSC by 10.8% compared to baseline and exhibits good generalization to clinical small-scale thyroid nodule datasets. The proposed MRDB can effectively improve thyroid nodule segmentation accuracy and has great potential for clinical applications.

Technical strategy for reapplying coatings on solar tower receiver tubes using concentrated flux

Reapplying the coating is crucial to restore optimal receiver performance. However, the heat treatment must be conducted onsite since the receiver cannot be removed from the tower. This study explored effective strategies for maximizing the coating’s performance after reapplication. The impact of different aimpoint strategies on the receiver’s surface temperature was analyzed using the heliostat field and the three-dimensional receiver model. Experimental tests were conducted using a solar lamp to study temperature variation curves of coatings on different tube panels under various aiming strategies, and absorptance was then measured. The optimal recoating strategy was determined through a comprehensive analysis of the system’s power generation and its Levelized Cost of Energy across its entire lifecycle. The “image size priority” strategy led to significant temperature variations among tube panels and within each panel. In contrast, the “segmented target flux” strategy effectively minimizes temperature differences, ensuring that over 91.25 % of the receiver tube panel reached temperatures above 550 °C. Comparatively, under the “image size priority” and “segmented target flux” strategies, the coating’s absorptance at the molten salt inlet panel is lower by 1.84 % and 0.10 %, respectively, compared to the standard sample. The effectiveness of the “segmented target flux” strategy becomes more pronounced with shorter recoating intervals. The research contributes to enhancing the maintenance performance of the coating and the operational efficiency of the receiver.

Detection Method for Bolt Missing Pins of Transmission Line Based on Improved SSD Network

The bolt pin structure plays a role in connecting and fixing various components in transmission lines, and the pins are prone to detachment, which may lead to the disintegration of key components and cause serious consequences. The bolt pin target size in the transmission line inspection image is small, the background is complex, and the detection effect using traditional object detection algorithms is poor. This article proposes a method for detecting missing bolts in transmission lines based on an improved SSD (Single Shot MultiBox Detector) network. Using the traditional SSD network as the foundation, a feature fusion module is first added to the network to enrich the semantic information of low-level feature maps and the detail information of high-level feature maps through the mutual fusion of high-level and low-level feature maps. Secondly, embedding a channel attention mechanism module enhances the channel features corresponding to the bolt pin structure and suppresses channel features that are irrelevant to the background; At the same time, the introduction of Focal Loss loss function reduces the unevenness of positive and negative sample classification. Finally, experiments were conducted on the collected bolt missing pin detection dataset, and the results showed that the method proposed in this paper has good performance in detecting missing pins, and the detection accuracy has been significantly improved.

DSM Reconstruction from Uncalibrated Multi-View Satellite Stereo Images by RPC Estimation and Integration

In this paper, we propose a 3D Digital Surface Model (DSM) reconstruction method from uncalibrated Multi-view Satellite Stereo (MVSS) images, where Rational Polynomial Coefficient (RPC) sensor parameters are not available. While recent investigations have introduced several techniques to reconstruct high-precision and high-density DSMs from MVSS images, they inherently depend on the use of geo-corrected RPC sensor parameters. However, RPC parameters from satellite sensors are subject to being erroneous due to inaccurate sensor data. In addition, due to the increasing data availability from the internet, uncalibrated satellite images can be easily obtained without RPC parameters. This study proposes a novel method to reconstruct a 3D DSM from uncalibrated MVSS images by estimating and integrating RPC parameters. To do this, we first employ a structure from motion (SfM) and 3D homography-based geo-referencing method to reconstruct an initial DSM. Second, we sample 3D points from the initial DSM as references and reproject them to the 2D image space to determine 3D–2D correspondences. Using the correspondences, we directly calculate all RPC parameters. To overcome the memory shortage problem while running the large size of satellite images, we also propose an RPC integration method. Image space is partitioned to multiple tiles, and RPC estimation is performed independently in each tile. Then, all tiles’ RPCs are integrated into the final RPC to represent the geometry of the whole image space. Finally, the integrated RPC is used to run a true MVSS pipeline to obtain the 3D DSM. The experimental results show that the proposed method can achieve 1.455 m Mean Absolute Error (MAE) in the height map reconstruction from multi-view satellite benchmark datasets. We also show that the proposed method can be used to reconstruct a geo-referenced 3D DSM from uncalibrated and freely available Google Earth imagery.

Parallel Lossless Compression of Raw Bayer Images on FPGA-Based High-Speed Camera

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb–Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on AMD-Xilinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high-resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping the total memory requirements low due to real-time processing.

Early Cervical Cancer Diagnosis with SWIN-Transformer and Convolutional Neural Networks.

Introduction: Early diagnosis of cervical cancer at the precancerous stage is critical for effective treatment and improved patient outcomes. Objective: This study aims to explore the use of SWIN Transformer and Convolutional Neural Network (CNN) hybrid models combined with transfer learning to classify precancerous colposcopy images. Methods: Out of 913 images from 200 cases obtained from the Colposcopy Image Bank of the International Agency for Research on Cancer, 898 met quality standards and were classified as normal, precancerous, or cancerous based on colposcopy and histopathological findings. The cases corresponding to the 360 precancerous images, along with an equal number of normal cases, were divided into a 70/30 train-test split. The SWIN Transformer and CNN hybrid model combines the advantages of local feature extraction by CNNs with the global context modeling by SWIN Transformers, resulting in superior classification performance and a more automated process. The hybrid model approach involves enhancing image quality through preprocessing, extracting local features with CNNs, capturing the global context with the SWIN Transformer, integrating these features for classification, and refining the training process by tuning hyperparameters. Results: The trained model achieved the following classification performances on fivefold cross-validation data: a 94% Area Under the Curve (AUC), an 88% F1 score, and 87% accuracy. On two completely independent test sets, which were never seen by the model during training, the model achieved an 80% AUC, a 75% F1 score, and 75% accuracy on the first test set (precancerous vs. normal) and an 82% AUC, a 78% F1 score, and 75% accuracy on the second test set (cancer vs. normal). Conclusions: These high-performance metrics demonstrate the models' effectiveness in distinguishing precancerous from normal colposcopy images, even with modest datasets, limited data augmentation, and the smaller effect size of precancerous images compared to malignant lesions. The findings suggest that these techniques can significantly aid in the early detection of cervical cancer at the precancerous stage.

Isolating the effects of body size and sexualisation in social media images on body image-related constructs among young women

This study systematically separated the effects of body size and sexualisation in social media images on women’s body image. Young women (18–25 years, N = 194) viewed 10 social media images of either (1) sexualised women in larger bodies; (2) non-sexualised women in larger bodies; (3) sexualised women in smaller bodies; or (4) non-sexualised women in smaller bodies. Participants completed pre- and post-exposure measures of body image and related constructs. Thin ideal internalisation was examined as a moderator of those effects. The body size of the women in the images was more influential than how sexualized their bodies were presented. Regardless of sexualisation, viewing images of women with smaller bodies was negative for appearance satisfaction, negative mood, and body appreciation, and viewing women with larger bodies was positive for body appreciation and neutral for appearance satisfaction and mood. The impact of body size on appearance satisfaction was stronger for those high in thin ideal internalisation. Women with larger bodies were perceived more positively than those with smaller bodies. However, participants’ self-objectification increased in all conditions. Thus, while viewing images of women with larger bodies provides promise for improving social media, further research is needed before it is recommended broadly.

A Longitudinal Study on Body Image Perception and Size among Italian Early Adolescents: Changes over Time and Discrepancies between Genders.

The discrepancy between the current perceived body image (BI) and beauty ideals leads to dissatisfaction, which is believed to be common among adolescents. This study aimed to investigate the stability in BI perceptions and dissatisfaction during early adolescence. Another aim was to highlight differences in dissatisfaction according to Body Mass Index (BMI) and inconsistencies in weight status perception. Two hundred and nine participants (mean age at first survey: 11.33 ± 0.38 years) were enrolled in this longitudinal study with three years of follow-up. Data on size and BI perceptions were collected through individual interviews. Stature and weight were directly measured. Findings indicated significant changes over three years in anthropometric traits but not in weight status prevalence or BI perception and dissatisfaction, except for the ideal figure in males and weight control in females. The results also indicated a significant difference in BI perception and dissatisfaction by BMI categories. Self-reported body measurements were found to be unreliable with a weak-to-moderate agreement between self-perceived and actual weight status. BI perception appears quite stable across the three years considered. Dissatisfaction is similar in both genders, although a tendency toward different gender aesthetic ideals is already appreciable in early adolescence. We suggest that the lower frequency of normal-weight adolescents compared with peers in previous studies is attributable to the effects of the recent pandemic. Given the growing dissatisfaction with increasing BMI and misinterpretations of weight status, school actions to promote a healthy lifestyle and positive BI should be undertaken.

Fusion of visible and fluorescence imaging through deep neural network for color value prediction of pelletized red peppers.

A non-destructive method for determining the color value of pelletized red peppers is crucial for pepper processing factories. This study aimed to investigate the potentiality of visible and fluorescence images for the determination of color value of pelletized red pepper. The imaging problem, caused by the cylindrical shape and irregular cross-sectional features of the pelletized red peppers, was reduced through the extraction of an approximate plane region. To integrate the information in the visible and fluorescence images, a baseline convolutional neural network (CNN) architecture was designed and low level, middle level, and high level fusion models (denoted as LL-CNN, ML-CNN, and HL-CNN, respectively) were developed upon the baseline CNN. The effects of input image size and color space were examined. According to the training result, CNN fusion models were developed using visible image in L*a*b* color space and fluorescence image in RGB color space using 56×56 input image size. Among the three types of CNN fusion models, the HL-CNN obtained the best performance, resulting in Rv 2 of 0.828 and RMSEV of 0.351. This study suggests that the fusion of visible and fluorescence image through CNN is a practical approach to save testing time and replace traditional destructive method. The low cost and compact structure of the imaging systems can maintain the commercial appeal of pepper industry.

YOLO-RWY: A Novel Runway Detection Model for Vision-Based Autonomous Landing of Fixed-Wing Unmanned Aerial Vehicles

In scenarios where global navigation satellite systems (GNSSs) and radio navigation systems are denied, vision-based autonomous landing (VAL) for fixed-wing unmanned aerial vehicles (UAVs) becomes essential. Accurate and real-time runway detection in VAL is vital for providing precise positional and orientational guidance. However, existing research faces significant challenges, including insufficient accuracy, inadequate real-time performance, poor robustness, and high susceptibility to disturbances. To address these challenges, this paper introduces a novel single-stage, anchor-free, and decoupled vision-based runway detection framework, referred to as YOLO-RWY. First, an enhanced data augmentation (EDA) module is incorporated to perform various augmentations, enriching image diversity, and introducing perturbations that improve generalization and safety. Second, a large separable kernel attention (LSKA) module is integrated into the backbone structure to provide a lightweight attention mechanism with a broad receptive field, enhancing feature representation. Third, the neck structure is reorganized as a bidirectional feature pyramid network (BiFPN) module with skip connections and attention allocation, enabling efficient multi-scale and across-stage feature fusion. Finally, the regression loss and task-aligned learning (TAL) assigner are optimized using efficient intersection over union (EIoU) to improve localization evaluation, resulting in faster and more accurate convergence. Comprehensive experiments demonstrate that YOLO-RWY achieves AP50:95 scores of 0.760, 0.611, and 0.413 on synthetic, real nominal, and real edge test sets of the landing approach runway detection (LARD) dataset, respectively. Deployment experiments on an edge device show that YOLO-RWY achieves an inference speed of 154.4 FPS under FP32 quantization with an image size of 640. The results indicate that the proposed YOLO-RWY model possesses strong generalization and real-time capabilities, enabling accurate runway detection in complex and challenging visual environments, and providing support for the onboard VAL systems of fixed-wing UAVs.

Innovative strategies for intelligent services in smart libraries in the information age based on linear discriminant analysis

With the advent of the information age, to provide better services and ensure the security management of libraries, intelligent facial recognition technology has gradually become a hot research direction in library management. Meanwhile, to further improve the comprehensive performance of facial recognition, this study attempts to integrate principal component analysis and linear discriminant analysis on the basis of analyzing the framework of recognition technology. Afterwards, it introduced support vector machines for recognition and classification, and proposed a new recognition model. The experimental results show that the recognition accuracy of the proposed model is up to 97 % in the ORL dataset and 94 % in the Yale dataset. The recognition error rate is as low as 0.1 % when the number of training samples is 215 and the number of iterations is 200. The model has the best recognition performance when the image size is 25 × 25 mm and the number of noises is 10. In addition, the model is particularly effective in recognition on single person color or gray images, with the highest P-value of 98.7 %, the highest R-value of 98.8 %, and the highest F1-value of 97.5 %. These results show that the proposed model significantly improves the accuracy and robustness of face recognition, and provides strong technical support for intelligent service innovation in smart libraries.

PSNR and SSIM Performance Analysis of Schur Decomposition for Imperceptible Steganography

Purpose: This research examines how well Schur decomposition-based steganography can hide data in digital images without being noticed, while also keeping the image quality high and keeping the hidden information safe. Methods: The study starts by choosing regular test images (Lena, Plane, Peppers, Cameraman, Baboon) to use for hiding messages in. The Schur decomposition is used to hide information within images in a subtle way. To test how well the new method works, we added Gaussian noise and Salt & Pepper noise after embedding. The quality of the image is determined by looking at the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. Result: The research shows that Schur decomposition results in very good SSIM values (greater than 0.92) and high PSNR scores (as high as 90.27 dB) for various image sizes (64x64, 128x128, 256x256). This means that the quality of the images is not greatly reduced even after steganography is applied. Novelty: This research introduces a unique use of Schur decomposition for hiding data in images without affecting their quality. The study highlights how this method can securely hide information in digital media, which could be really useful for improving steganography techniques in the future. Future studies should concentrate on making improvements to Schur decomposition-based steganography, especially for bigger images. One possibility is to create adaptive methods that can change how images are hidden based on their content. This could make it harder for others to detect and analyze hidden information in the images.

VISTA: vision improvement via split and reconstruct deep neural network for fundus image quality assessment

AbstractWidespread eye conditions such as cataracts, diabetic retinopathy, and glaucoma impact people worldwide. Ophthalmology uses fundus photography for diagnosing these retinal disorders, but fundus images are prone to image quality challenges. Accurate diagnosis hinges on high-quality fundus images. Therefore, there is a need for image quality assessment methods to evaluate fundus images before diagnosis. Consequently, this paper introduces a deep learning model tailored for fundus images that supports large images. Our division method centres on preserving the original image’s high-resolution features while maintaining low computing and high accuracy. The proposed approach encompasses two fundamental components: an autoencoder model for input image reconstruction and image classification to classify the image quality based on the latent features extracted by the autoencoder, all performed at the original image size, without alteration, before reassembly for decoding networks. Through post hoc interpretability methods, we verified that our model focuses on key elements of fundus image quality. Additionally, an intrinsic interpretability module has been designed into the network that allows decomposing class scores into underlying concepts quality such as brightness or presence of anatomical structures. Experimental results in our model with EyeQ, a fundus image dataset with three categories (Good, Usable, and Rejected) demonstrate that our approach produces competitive outcomes compared to other deep learning-based methods with an overall accuracy of 0.9066, a precision of 0.8843, a recall of 0.8905, and an impressive F1-score of 0.8868. The code is publicly available at https://github.com/saifalkhaldiurv/VISTA_-Image-Quality-Assessment.