Efficient single-pixel imaging encrypted transmission based on 3D Arnold transformation

Single-pixel imaging(SPI)can transform 2D or 3D image data into 1D light signals,which offers promising prospects for image compression and transmission.However,during data communication these light signals in public channels will easily draw the attention of eavesdroppers.Here,we introduce an efficient encryption method for SPI data transmission that uses the 3D Arnold transformation to directly disrupt 1D single-pixel light signals and utilizes the elliptic curve encryption algorithm for key transmission.This encryption scheme immediately employs Hadamard patterns to illuminate the scene and then utilizes the 3D Arnold transformation to permutate the 1D light signal of single-pixel detection.Then the transformation parameters serve as the secret key,while the security of key exchange is guaranteed by an elliptic curve-based key exchange mechanism.Compared with existing encryption schemes,both computer simulations and optical experiments have been conducted to demonstrate that the proposed technique not only enhances the security of encryption but also eliminates the need for complicated pattern scrambling rules.Additionally,this approach solves the problem of secure key transmission,thus ensuring the security of information and the quality of the decrypted images.

Remote sensing image encryption algorithm based on novel hyperchaos and an elliptic curve cryptosystem

Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.

The Application Value of Ultrasound Imaging in the Differential Diagnosis of Benign and Malignant Breast Nodules of BI-RADS 3 and Above

Objective:To explore the diagnostic value of ultrasound imaging for breast nodules of breast imaging-reporting and data system(BI-RADS)category 3 and above.Methods:From June 2021 to July 2022,163 patients with breast nodules of BI-RADS 3 or above were selected as the research subjects.After pathological diagnosis,24 cases were malignant breast nodules of BI-RADS 3 or above,while 139 cases were benign breast nodules of BI-RADS 3 or above.The diagnosis rate of malignant and benign breast nodules of BI-RADS 3 or above,including 95%CI,was observed and analyzed.Results:The malignant and benign detection rates of conventional ultrasound were 88.63%and 75.00%,respectively,and the malignant and benign detection rates of ultrasound imaging were 93.18%and 87.50%,respectively,with 95%CIs greater than 0.7.Conclusion:Ultrasound imaging can help improve the diagnostic accuracy of benign and malignant breast nodules of BI-RADS 3 and above and reduce the misdiagnosis rate.

An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.

Integrating Highly Spatial Satellite Image for 3D Buildings Modelling Using Geospatial Algorithms and Architecture Environment

The growing demand for current and precise geographic information that pertains to urban areas has given rise to a significant interest in digital surface models that exhibit a high level of detail. Traditional methods for creating digital surface models are insufficient to reflect the details of earth’s features. These models only represent three-dimensional objects in a single texture and fail to offer a realistic depiction of the real world. Furthermore, the need for current and precise geographic information regarding urban areas has been increasing significantly. This study proposes a new technique to address this problem, which involves integrating remote sensing, Geographic Information Systems (GIS), and Architecture Environment software environments to generate a detailed three-dimensional model. The processing of this study starts with: 1) Downloading high-resolution satellite imagery; 2) Collecting ground truth datasets from fieldwork; 3) Imaging nose removing; 4) Generating a Two-dimensional Model to create a digital surface model in GIS using the extracted building outlines; 5) Converting the model into multi-patch layers to construct a 3D model for each object separately. The results show that the 3D model obtained through this method is highly detailed and effective for various applications, including environmental studies, urban development, expansion planning, and shape understanding tasks.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Reduced Imaging Time and Improved Image Quality of 3D Isotropic T2-Weighted Magnetic Resonance Imaging with Compressed Sensing for the Female Pelvis

This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images.

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image.The classification accuracy is mainly influenced by the feature sets that are selected to classify the image.In the past,handcrafted feature sets are used which are not adaptive for different image domains.To overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Firefly Algorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose.For extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions.For selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based technique.Fivefold Multiclass Support Vector Machine(MSVM)is used for classification purpose.Experiments con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.

Image Non-Uniformity Correction in 3T Gd-EOB-DTPA-Enhanced Magnetic Resonance Imaging: Comparison among Different Software Versions

Background: Non-uniformity in signal intensity occurs commonly in magnetic resonance (MR) imaging, which may pose substantial problems when using a 3T scanner. Therefore, image non-uniformity correction is usually applied. Purpose: To compare the correction effects of the phased-array uniformity enhancement (PURE), a calibration-based image non-uniformity correction method, among three different software versions in 3T Gd-EOB-DTPA-enhanced MR imaging. Material and Methods: Hepatobiliary-phase images of a total of 120 patients who underwent Gd-EOB-DTPA-enhanced MR imaging on the same 3T scanner were analyzed retrospectively. Forty patients each were examined using three software versions (DV25, DV25.1, and DV26). The effects of PURE were compared by visual assessment, histogram analysis of liver signal intensity, evaluation of the spatial distribution of correction effects, and evaluation of quantitative indices of liver parenchymal enhancement. Results: The visual assessment indicated the highest uniformity of PURE-corrected images for DV26, followed by DV25 and DV25.1. Histogram analysis of corrected images demonstrated significantly larger variations in liver signal for DV25.1 than for the other two versions. Although PURE caused a relative increase in pixel values for central and lateral regions, such effects were weaker for DV25.1 than for the other two versions. In the evaluation of quantitative indices of liver parenchymal enhancement, the liver-to-muscle ratio (LMR) was significantly higher for the corrected images than for the uncorrected images, but the liver-to-spleen ratio (LSR) showed no significant differences. For corrected images, the LMR was significantly higher for DV25 and DV26 than for DV25.1, but the LSR showed no significant differences among the three versions. Conclusion: There were differences in the effects of PURE among the three software versions in 3T Gd-EOB-DTPA-enhanced MR imaging. Even if the non-uniformity correction method has the same brand name, correction effects may differ depending on the software version, and these differences may affect visual and quantitative evaluations.

Analysis of Remotely Sensed Imagery and Architecture Environment for Modelling 3D Detailed Buildings Using Geospatial Techniques

The use of three-dimensional maps is more effective than two-dimensional maps in representing the Earth’s surface. However, the traditional methods used to create digital surface models are not efficient for capturing the details of Earth’s features. This is because they represent only three-dimensional objects in a single texture and do not provide a realistic representation of the real world. Additionally, there is a growing demand for up-to-date and accurate geo-information, particularly in urban areas. To address this challenge, a new technique is proposed in this study that involves integrating remote sensing, Geographic Information System, and Architecture Environment software to generate a highly-detailed three-dimensional model. The method described in this study includes several steps such as acquiring high-resolution satellite imagery, gathering ground truth data, performing radiometric and geometric corrections during image preprocessing, producing a 2D map of the region of interest, constructing a digital surface model by extending the building outlines, and transforming the model into multi-patch layers to create a 3D model for each object individually. The research findings indicate that the digital surface model obtained with comprehensive information is suitable for different purposes, such as environmental research, urban development and expansion planning, and shape recognition tasks.

Construction of Ship Target Image Library Based on 3DS MAX and AP Algorithm

To achieve accurate classification and recognition of ship target types,it is necessary to establish a sample library of ship targets to be identified.On the basis of exploring the principles of building a ship target image library,the paper determines the sample set.Using 3DS MAX software as the platform,combined with the accurate 3D model of the ship in an offline state,the software fully utilizes its own rendering and animation functions to achieve the automatic generation of multi-view and multi-scale views of ship targets.To reduce the storage capacity of the image database,a construction method of the ship target image database based on the AP algorithm is presented.The algorithm can obtain the optimal cluster number,reduce the data storage capacity of the image database,and save the calculation amount for the subsequent matching calculation.

A network lightweighting method for difficult segmentation of 3D medical images

Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.

Estimation of leaf area index from high resolution ZY-3 satellite imagery in a catchment dominated by Larix principis-rupprechtii,northern China

Leaf area index(LAI) is a key factor that determines a forest ecosystem's net primary production and energy exchange between the atmosphere and land surfaces.LAI can be measured in many ways, but there has been little research to compare LAI estimated by different methods. In this study, we compared the LAI results from two different approaches, i.e., the dimidiate pixel model(DPM) and an empirical statistic model(ESM) using ZY-3 high-accuracy satellite images validated by field data. We explored the relationship of LAI of Larix principis-rupprechtii Mayr plantations with topographic conditions. The results show that DPM improves the simulation of LAI(r = 0.86,RMSE = 0.57) compared with ESM(r = 0.62, RMSE =0.79). We further concluded that elevation and slope significantly affect the distribution of LAI. The maximum peak of LAI appeared at an aspect of east and southeast at an elevation of 1700–2000 m. Our results suggest that ZY-3 can satisfy the needs of quantitative monitoring of leaf area indices in small-scale catchment areas. DPM provides a simple and accurate method to obtain forest vegetation parameters in the case of non-ground measurement points.

Comparison between Plane and Stereo Block Adjustment for ZY-3 Satellite Images

Block adjustment for satellite images cannot be solved with weak convergence geometric conditions,therefore a plane block adjustment method to improve the targeting precision of images is proposed utilizing DEM as height constraint plane block adjustment method.First,a rational function model with affine transformation is selected as the mathematical model of the satellite image plane block adjustment.Second,to update the ground coordinates of tie points(TPs),the plane coordinates of TPs are only solved in the adjustment process.Elevation values are obtained by using DEM interpolation.Finally,the plane coordinates of all TPs and orientation parameters of all satellite images are solved through plane block adjustment with a few ground control points ZY-3 nadir images for two regions are tested for plane block adjustment while ZY-3 forward-nadir-back images of the same two regions are tested for stereo block adjustment.A comparison indicates that almost the same accuracy can be obtained with plane block adjustment support using a 1∶50 000 DEM and stereo block adjustment for ZY-3 images.For ZY-3 nadir images,almost no loss of plane block adjustment accuracy occurred when global DEM with 1 km grid and SRTM with 90 m grid replaced the 1∶50 000 DEM as elevation control,.Test results demonstrate the effectiveness and feasibility of the plane block adjustment method.

MRI T_2 star mapping、T_1 images与3D DESS融合图在隐匿性膝关节软骨损伤中的应用

目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。

2 cm以上的C-TI-RADS 3类甲状腺结节的临床特点分析

目的统计2 cm以上的中国版甲状腺影像报告及数据系统(C-TI-RADS)3类甲状腺结节的临床特点,并探究其与性别、结节成分、对侧是否有癌、是否弥漫性回声改变、甲状腺过氧化物酶抗体(thyroid peroxidase antibody,TPOAB)、甲状腺球蛋白抗体(anti-thyroglobulin antibodies,TGAB)等的相关性。方法回顾性分析2022年9月-2023年3月我科收治的甲状腺超声C-TI-RADS 3类且最大直径≥2 cm的94例甲状腺结节患者(均接受细胞病理和/或组织病理检查)的临床病理学信息。统计TBSⅠ类、良性、低风险肿瘤、恶性的比例,并比较性别、结节成分、对侧是否有癌、是否弥漫性回声改变、TPOAB、TGAB等临床特点在良性、低风险肿瘤、恶性三组中的比例有无统计学差异。结果排除7例TBSⅠ类患者,87例结节病理明确患者中,良性72例(细胞学38例、组织学34例)、低风险肿瘤5例(细胞学2例、组织学3例)、恶性10例(PTC 8例、FTC 1例、MTC 1例)。不同病理类型之间,结节成分(囊实性/实性)组间差异具有显著性,差异有统计学意义(χ^(2)=10.369,P=0.006);性别、是否弥漫性回声改变、对侧是否有癌、TPOAB、TGAB组间差异无统计学意义(P均>0.05)。进一步分析结节成分与病理类型的关系,结果表明,低风险肿瘤相对于良性结节实性比例更高,差异有统计学意义(χ^(2)=9.571,P=0.002);而恶性结节相对于低风险肿瘤(χ^(2)=2.143,P=0.143),恶性结节相对于良性结节(χ^(2)=2.165,P=0.141)囊实性比例差异均无统计学意义。结论虽然各种版本的甲状腺影像报告与数据系统均将TI-RADS 3级结节认定为良性可能,但恶性结节在C-TI-RADS 3类甲状腺结节中仍占一定比例,需要重视诸如囊实性结节、甲状腺滤泡性肿瘤、甲状腺髓样癌等超声征象不典型的甲状腺结节。在评估结节良恶性时,超声引导下细针穿刺细胞病理学检查是必要的,需要重视标本不满意或无法诊断的情况,提高诊断的准确性。

Integral imaging-based tabletop light field 3D display with large viewing angle

Light field 3D display technology is considered a revolutionary technology to address the critical visual fatigue issues in the existing 3D displays.Tabletop light field 3D display provides a brand-new display form that satisfies multi-user shared viewing and collaborative works,and it is poised to become a potential alternative to the traditional wall and portable display forms.However,a large radial viewing angle and correct radial perspective and parallax are still out of reach for most current tabletop light field 3D displays due to the limited amount of spatial information.To address the viewing angle and perspective issues,a novel integral imaging-based tabletop light field 3D display with a simple flat-panel structure is proposed and developed by applying a compound lens array,two spliced 8K liquid crystal display panels,and a light shaping diffuser screen.The compound lens array is designed to be composed of multiple three-piece compound lens units by employing a reverse design scheme,which greatly extends the radial viewing angle in the case of a limited amount of spatial information and balances other important 3D display parameters.The proposed display has a radial viewing angle of 68.7°in a large display size of 43.5 inches,which is larger than the conventional tabletop light field 3D displays.The radial perspective and parallax are correct,and high-resolution 3D images can be reproduced in large radial viewing positions.We envision that this proposed display opens up possibility for redefining the display forms of consumer electronics.

Centimeter-sized Cs_(3)Cu_(2)I_(5)single crystals grown by oleic acid assisted inverse temperature crystallization strategy and their films for high-quality X-ray imaging

Low-dimensional halide perovskites have become the most promising candidates for X-ray imaging,yet the issues of the poor chemical stability of hybrid halide perovskite,the high poisonousness of lead halides and the relatively low detectivity of the lead-free halide perovskites which seriously restrain its commercialization.Here,we developed a solution inverse temperature crystal growth(ITCG)method to bring-up high quality Cs_(3)Cu_(2)I_(5)crystals with large size of centimeter order,in which the oleic acid(OA)is introduced as an antioxidative ligand to inhibit the oxidation of cuprous ions effieiently,as well as to decelerate the crystallization rate remarkalby.Based on these fine crystals,the vapor deposition technique is empolyed to prepare high quality Cs_(3)Cu_(2)I_(5)films for efficient X-ray imaging.Smooth surface morphology,high light yields and short decay time endow the Cs_(3)Cu_(2)I_(5)films with strong radioluminescence,high resolution(12 lp/mm),low detection limits(53 nGyair/s)and desirable stability.Subsequently,the Cs_(3)Cu_(2)I_(5)films have been applied to the practical radiography which exhibit superior X-ray imaging performance.Our work provides a paradigm to fabricate nonpoisonous and chemically stable inorganic halide perovskite for X-ray imaging.

基于ZY-3和Landsat-8影像的内蒙古北山地区地层构造解译

内蒙古北山地区属于典型的戈壁荒漠区,基岩裸露,植被不发育,构造复杂,遥感技术在该地区的应用能起到事半功倍的作用。本文基于ZY-3和Landsat-8 OLI影像,选择内蒙古北山基东地区为研究区,利用ZY-3和Landsat-8 OLI数据,并以数据协同理论为基础,运用图像融合、彩色合成、主成分分析、空间滤波等岩性、空间信息增强技术,对研究区展开遥感地质解译,建立该地区地层、构造遥感解译标志。经野外验证,多数解译标志准确可靠,解译地质界线和构造信息与野外实际情况吻合度较高,极大地提高了填图工作效率。研究结果表明,ZY-3和Landsat-8 OLI协同数据在该地区地质调查中具有广阔的应用前景。