Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion

In this paper, to improve the slow processing speed of the rule-based visible and NIR (near-infrared) image synthesis method, we present a fast image fusion method using DenseFuse, one of the CNN (convolutional neural network)-based image synthesis methods. The proposed method applies a raster scan algorithm to secure visible and NIR datasets for effective learning and presents a dataset classification method using luminance and variance. Additionally, in this paper, a method for synthesizing a feature map in a fusion layer is presented and compared with the method for synthesizing a feature map in other fusion layers. The proposed method learns the superior image quality of the rule-based image synthesis method and shows a clear synthesized image with better visibility than other existing learning-based image synthesis methods. Compared with the rule-based image synthesis method used as the target image, the proposed method has an advantage in processing speed by reducing the processing time to three times or more.     

Multi-Focus Image Fusion Based on Hessian Matrix Decomposition and Salient Difference Focus Detection

Multi-focus image fusion integrates images from multiple focus regions of the same scene in focus to produce a fully focused image. However, the accurate retention of the focused pixels to the fusion result remains a major challenge. This study proposes a multi-focus image fusion algorithm based on Hessian matrix decomposition and salient difference focus detection, which can effectively retain the sharp pixels in the focus region of a source image. First, the source image was decomposed using a Hessian matrix to obtain the feature map containing the structural information. A focus difference analysis scheme based on the improved sum of a modified Laplacian was designed to effectively determine the focusing information at the corresponding positions of the structural feature map and source image. In the process of the decision-map optimization, considering the variability of image size, an adaptive multiscale consistency verification algorithm was designed, which helped the final fused image to effectively retain the focusing information of the source image. Experimental results showed that our method performed better than some state-of-the-art methods in both subjective and quantitative evaluation.     

基于激光点云多条件约束的相机检校方法

为了克服相机检校对二维/三维检校场的依赖,提出一种基于激光点云多条件约束的相机检校方法.该方法通过对相机获取的多视影像进行光束法平差获得初始相机参数;利用影像点云与其最邻近的激光点云之间的位置关系,以共线方程为基础模型,建立多条件约束的相机检校数学模型;使用不等式约束的最小二乘方法平差迭代解算相机参数.将本文方法与基于...     

DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.     

新型锍鎓盐类光生酸剂的合成及产酸性能研究

成功合成了两种新型锍鎓盐类光生酸剂,其结构经¹1HNMR和MS分析确认,并对其基本物性及在405、365nm光下乙腈溶液中的分解及产酸性能进行了研究,通过计算得出了分解及产酸量子产率.结果表明,两种化合物有较高的热分解温度和在常用有机溶剂中有较好的溶解性;在405nm光源下,4-(9′-苯基蒽基)苯基三氟甲磺酸锍鎓盐(PAGS1)和4-(4′-N,N-二乙基-1′-苯乙烯基)苯基三氟甲磺酸锍鎓盐(PAGS2)的分解量子产率分别为10%和15%,产酸量子产率为8.1%和13%;但在365nm光源下,分解及产酸量子产率均很低,说明两种光生酸剂对于405nm波长的光较敏感,适宜作为405nm光源下的光生酸剂.     

Deep Image Steganography Using Transformer and Recursive Permutation

Image steganography, which usually hides a small image (hidden image or secret image) in a large image (carrier) so that the crackers cannot feel the existence of the hidden image in the carrier, has become a hot topic in the community of image security. Recent deep-learning techniques have promoted image steganography to a new stage. To improve the performance of steganography, this paper proposes a novel scheme that uses the Transformer for feature extraction in steganography. In addition, an image encryption algorithm using recursive permutation is proposed to further enhance the security of secret images. We conduct extensive experiments to demonstrate the effectiveness of the proposed scheme. We reveal that the Transformer is superior to the compared state-of-the-art deep-learning models in feature extraction for steganography. In addition, the proposed image encryption algorithm has good attributes for image security, which further enhances the performance of the proposed scheme of steganography.     

Lossless Medical Image Compression by Using Difference Transform

This paper introduces a new method of compressing digital images by using the Difference Transform applied in medical imaging. The Difference Transform algorithm performs the decorrelation process of image data, and in this way improves the encoding process, achieving a file with a smaller size than the original. The proposed method proves to be competitive and in many cases better than the standards used for medical images such as TIFF or PNG. In addition, the Difference Transform can replace other transforms like Cosine or Wavelet.     

应用不同光谱技术,动物RBC,Hb及人皮肤表面血流氧化-还原状态检测

目的:检测与分析实验动物血液RBC(RBC·O2,RBC·CO2),Hb(HbO2,HbCO2)和人体皮肤表面流动血液氧化·还原状态的成像与未成像可见光谱领域OD值特征,并为该技术对白癜风病表皮黑色素颗粒检测中的应用尊定基础。方法:利用不同光谱技术和invitro和invivo检测手段,统计分析血液不同状态下波长与位置的OD值信息。结果:invitro检测:动物血液Hb·O2和RBC·O2两者在可见领域均有367,414(Soret带)nm与541,576(Q带)nm的吸收峰位;血液Hb·CO2和RBC·CO2均有432(Soret带)与和553(Q带)nm的波长吸收峰位;血液RBC状态和Hb溶血状态波长吸收峰位无改变,只是在氧化与还原状态下有完全独立的吸收峰位,血液RBC状态和Hb溶血状态波长吸光度OD值之间,有显著性差异(p<0·01)。浓度为1·5×107cell·mL-1的RBC·O2和Hb·O2在576nm的吸收峰位吸光度(y)与红细胞浓度(x)做成两条回归曲线:既,Hb·O2(b1)^y=0·05 0·983x;RBC·O2(b2)y^=0·127 1·934x,两者之间差异有显著性(p<0·01)。invivo检测:在人手背皮肤表面ImSpector图像中RBC·O2状态在540,576nm,RBC·CO2状态在555和755nm处有吸收峰。选择(a:指甲,b:指,c:手背)三个点位分别进行波长检测,每点(n=10)545nm吸收峰的平均OD值,依次为0·83±0·001,0·73±0·001和0·62±0·001,其三处测定点的OD值之间有显著性差异(p<0·01)。结论:invitro检测的RBC与Hb两者波长吸收峰位不变,但吸光度OD值不同,认为RBC状态测定结果更接近于活体组织血管内原始状态。invivo检测对人体无任何侵袭与损伤,灵敏度高,测试时间短,并且同时获得被测样品的波长与位置信息画面等优势,有望表皮中黑素等有色颗粒的直接检测。     

基于径向基函数神经网络的高光谱遥感图像分类

从径向基函数神经网络的理论出发,针对高光谱数据的特点,设计了有效的特征提取模型,再与径向基函数神经网络的输入层连接,建立了一个新的径向基函数神经网络的高光谱遥感影像分类模型,并用国产OMISII传感器获得的64波段数据进行试验。首先进行了最小噪声分离变换,提取了1～20个分量的数据,使用提取后的数据（20维）、提取后数据的纹理变换（20维）和主成分分析的前（20维）,组成了60维向量数据进行分类处理,这种分类器结构简单、容易训练、收敛速度快,其分类精度达到69.27%,高于BP神经网络分类算法(51.20%)以及常用的最小距离分类（MDC）算法(40.88%)。通过对结果和过程进行分析,实验证明径向基函数神经网络在高光谱遥感分类中具有较好的适用性。     

近红外高光谱成像技术在筛查保健食品中违禁添加抗炎药物双氯芬酸钠的研究

双氯芬酸钠是一种非甾体抗炎药,抗炎症效果较好,抗炎化学药物双氯芬酸钠有可能违禁添加到缓解炎症保健食品中.目前已有一些文献研究水、肉及牛奶等中的药物双氯芬酸钠,主要利用高效液相色谱、表面增强拉曼及电化学等方法,需要相对复杂的预处理过程,操作步骤相对复杂,耗时.为了快速、无损检测保健食品中违禁添加物的成分,探索建立采用10...