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.     

基于梯度树的光学层析图像重建方法

光学相干层析成像技术是一种新型成像方法,在生物医学和材料等许多领域有广泛的应用.介绍了一种基于梯度树的迭代图像重建方法,讨论了输运模型下含空洞状区域的图像重建方法.证明了基于输运模型的图像重建能克服扩散方程在非散射区域的重建弊端,准确地重建光学层析图像.     

像增强管老炼工艺研究与分析

像增强管老炼工艺可以尽早排除管子的早期失效和可能出现的图像缺陷,有利于管子尽快达到稳定工作状态,起到工艺稳定和筛选的双重作用.根据新近研制成功的低噪声双碱阴极像增强管的结构和性能特点,设计了管子的老炼工艺规范,并在操作暗箱内通过无光照和有光照两种工作模式,对整管进行老炼工艺研究;同时,也对管子出现的图像缺陷进行了研究与分析,并给出了指导性的解决办法.     

一种机载天文导航星图模拟的实现方法

星图模拟是研究机载天文导航的关键技术之一.通过分析近些年国内外天文导航技术研究方面的资料,结合机载条件下的实际运行环境、天文导航基本原理和成像的主要影响因素,改进了CCD星敏感器的光轴指向、视场范围和观测星的确定方法,对星体成像位置和灰度的确定方法及背景光和曝光时间对成像效果的影响进行了优化,并建立了相应的模型.最后运用Matlab编程生成了所需星图,为进一步研究机载天文导航技术打下了基础.     

一种新的基于魔方变换的数字图像置乱加密算法

提出一种基于魔方变换的数字图像置乱加密算法,突破了传统的不改变图像像素值、仅通过重新排列图像像素点的位置实现图像置乱加密的方法,在三维空间上实现了对图像数据的置乱加密,打破了原有图像的所有特征.提出了一种新的密钥与魔方变化的编码对应法则,使该加密算法的加密密钥不受类型、大小的限制,具有密钥空间大的特点.仿真结果表明,该算法具有很好的加密、解密结果.