Research on the Fusion Methods with High Information Preservation

2007 ◽  
Author(s):  
Wenbo Li ◽  
Qiuwen Zhang ◽  
Yongchuan Zhang
Keyword(s):  
Information Preservation ◽  
Fusion Methods ◽  
High Information

scholarly journals Maximizing Image Information Using Multi-Chimera Transform Applied on Face Biometric Modality

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 115
Author(s):  
Ahmad Saeed Mohammad ◽  
Dhafer Zaghar ◽  
Walaa Khalaf
Keyword(s):  
Mobile Technology ◽  
Data Reduction ◽  
Research Field ◽  
File Size ◽  
Facial Image ◽  
Information Preservation ◽  
Media Data ◽  
Reconstructed Data ◽  
Face Biometric ◽  
High Information

With the development of mobile technology, the usage of media data has increased dramatically. Therefore, data reduction represents a research field to maintain valuable information. In this paper, a new scheme called Multi Chimera Transform (MCT) based on data reduction with high information preservation, which aims to improve the reconstructed data by producing three parameters from each 16×16 block of data, is proposed. MCT is a 2D transform that depends on constructing a codebook of 256 picked blocks from some selected images which have a low similarity. The proposed transformation was applied on solid and soft biometric modalities of AR database, giving high information preservation with small resulted file size. The proposed method produced outstanding performance compared with KLT and WT in terms of SSIM and PSNR. The highest SSIM was 0.87 for the proposed scheme MCT of the full image of AR database, while the existed method KLT and WT had 0.81 and 0.68, respectively. In addition, the highest PSNR was 27.23 dB for the proposed scheme on warp facial image of AR database, while the existed methods KLT and WT had 24.70 dB and 21.79 dB, respectively.

scholarly journals Fusion of Multispectral and Panchromatic Images via Spatial Weighted Neighbor Embedding

2019 ◽  
Vol 11 (5) ◽  
pp. 557 ◽  
Author(s):  
Kai Zhang ◽  
Feng Zhang ◽  
Shuyuan Yang
Keyword(s):  
Spatial Structure ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Euclidean Distance ◽  
High Spatial Resolution ◽  
Spectral Information ◽  
Spatial Structures ◽  
Information Preservation ◽  
Effective Technology ◽  
Fusion Methods

Fusing the panchromatic (PAN) image and low spatial-resolution multispectral (LR MS) images is an effective technology for generating high spatial-resolution MS (HR MS) images. Some image-fusion methods inspired by neighbor embedding (NE) are proposed and produce competitive results. These methods generally adopt Euclidean distance to determinate the neighbors. However, closer Euclidean distance is not equal to greater similarity in spatial structure. In this paper, we propose a spatial weighted neighbor embedding (SWNE) approach for PAN and MS image fusion, by exploring the similar manifold structures existing in the observed LR MS images to those of HR MS images. In SWNE, the spatial neighbors of the LR patch are found first. Second, the weights of these neighbors are estimated by the alternative direction multiplier method (ADMM), in which the neighbors and their weights are determined simultaneously. Finally, the HR patches are reconstructed by the sum of HR patches corresponding to the LR patches multiplying with their weights. Due to the introduction of spatial structures in objective function, outlier patches can be eliminated effectively by ADMM. Compared with other methods based on NE, more reasonable neighbor patches and their weights are estimated simultaneously. Some experiments are conducted on datasets collected by QuickBird and Geoeye-1 satellites to validate the effectiveness of SWNE, and the results demonstrate a better performance of SWNE in spatial and spectral information preservation.

scholarly journals Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

2019 ◽  
Vol 11 (22) ◽  
pp. 2691 ◽  
Author(s):  
Gang He ◽  
Jiaping Zhong ◽  
Jie Lei ◽  
Yunsong Li ◽  
Weiying Xie
Keyword(s):  
Spatial Information ◽  
Spectral Characteristics ◽  
Data Sets ◽  
Information Preservation ◽  
Fusion Methods ◽  
Adaptive Fusion ◽  
Spatial Domains ◽  
Fusion Approach ◽  
Better Than

Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.

Enhanced Security Authentication of WiMAX using EC3A

2017 ◽  
Vol 7 (8) ◽  
pp. 219
Author(s):  
Mohd Javed ◽  
Khaleel Ahmad ◽  
Ahmad Talha Siddiqui
Keyword(s):  
Cellular Automata ◽  
Elliptic Curve ◽  
Elliptic Curve Cryptography ◽  
Information Rate ◽  
Security Authentication ◽  
Encryption And Decryption ◽  
High Information

WiMAX is the innovation and upgradation of 802.16 benchmarks given by IEEE. It has numerous remarkable qualities, for example, high information rate, the nature of the service, versatility, security and portability putting it heads and shoulder over the current advancements like broadband link, DSL and remote systems. Though like its competitors the concern for security remains mandatory. Since the remote medium is accessible to call, the assailants can undoubtedly get into the system, making the powerless against the client. Many modern confirmations and encryption methods have been installed into WiMAX; however, regardless it opens with up different dangers. In this paper, we proposed Elliptic curve Cryptography based on Cellular Automata (EC3A) for encryption and decryption the message for improving the WiMAX security

Modern Radiodiagnostics Technology of Metastasises in Pelvic Lymph Nodes

2017 ◽  
pp. 100-108
Author(s):  
V. N. Diomidova ◽  
O. A. Еfimova
Keyword(s):  
Lymph Node ◽  
Magnetic Resonance ◽  
Lymph Nodes ◽  
Metastatic Lymph Node ◽  
Magnetic Resonance Tomography ◽  
Visual Images ◽  
Pelvic Lymph Nodes ◽  
High Information Content ◽  
Pelvic Cancer ◽  
High Information

The analysis of the diagnostic informativeness of modern radiodiagnosticis methods in determining metastatic lymph node of pelvic cancer gynecological organs according to domestic and foreign publications. At the present stage methods of obtaining visual images pelvic lymph nodes are radiodiagnostics technologies (radiological, ultrasound, magnetic resonance tomography, scintigraphic). The analysis has shown that the researches devoted to diagnostic informational content of modern methods of radiodiagnosis in a differentiation of nature of damage of pelvic lymph nodes aren't enough. According to the literature, the most rational and perspective method for radiodiagnosis metastatics lymph node is a magnetic resonance imaging due to the high information content and thus specificity. At the same time, the continued relevance of further study of methods of radiodiagnostics in order to find the optimal one for the assessment of pelvic lymph nodes.

Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications

2019 ◽  
Vol 14 (3) ◽  
pp. 178-189 ◽  
Author(s):  
Xiaoyang Jing ◽  
Qimin Dong ◽  
Ruqian Lu ◽  
Qiwen Dong
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Tertiary Structure ◽  
Prediction Methods ◽  
Learning Methods ◽  
Typical Application ◽  
Machine Learning Methods ◽  
Residue Contacts ◽  
Fusion Methods ◽  
Correlated Mutations

Background:Protein inter-residue contacts prediction play an important role in the field of protein structure and function research. As a low-dimensional representation of protein tertiary structure, protein inter-residue contacts could greatly help de novo protein structure prediction methods to reduce the conformational search space. Over the past two decades, various methods have been developed for protein inter-residue contacts prediction.Objective:We provide a comprehensive and systematic review of protein inter-residue contacts prediction methods.Results:Protein inter-residue contacts prediction methods are roughly classified into five categories: correlated mutations methods, machine-learning methods, fusion methods, templatebased methods and 3D model-based methods. In this paper, firstly we describe the common definition of protein inter-residue contacts and show the typical application of protein inter-residue contacts. Then, we present a comprehensive review of the three main categories for protein interresidue contacts prediction: correlated mutations methods, machine-learning methods and fusion methods. Besides, we analyze the constraints for each category. Furthermore, we compare several representative methods on the CASP11 dataset and discuss performances of these methods in detail.Conclusion:Correlated mutations methods achieve better performances for long-range contacts, while the machine-learning method performs well for short-range contacts. Fusion methods could take advantage of the machine-learning and correlated mutations methods. Employing more effective fusion strategy could be helpful to further improve the performances of fusion methods.

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