- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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New Evolutionary-Based Techniques for Image Registration
摘要： The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.
关键词： hybrid techniques,image recognition,image registration,firefly technique,evolutionary computing,affine perturbation,evolution strategies,mutual information
更新于2025-09-19 17:15:36
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Rationality Modeling of 3D Scanning Image of Digital Print Laser Based on Block Matching Texture Information Repair
摘要： We experimentally investigate mutual information and generalized mutual information for coherent optical transmission systems. The impact of the assumed channel distribution on the achievable rate is investigated for distributions in up to four dimensions. Single channel and wavelength-division multiplexing (WDM) transmission over transmission links with and without inline dispersion compensation are studied. We show that for conventional WDM systems without inline dispersion compensation, a circularly symmetric complex Gaussian distribution is a good approximation of the channel. For other channels, such as with inline dispersion compensation, this is no longer true and gains in the achievable information rate are obtained by considering more sophisticated four-dimensional (4D) distributions. We also show that for nonlinear channels, gains in the achievable information rate can also be achieved by estimating the mean values of the received constellation in four dimensions. The highest gain for such channels is seen for a 4D correlated Gaussian distribution.
关键词： mutual Information,digital communication,fiber nonlinear optics,optical fiber communication,Channel models
更新于2025-09-19 17:13:59
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Characterization of double-bounce scattering in RVoG scenarios using controlled HR-PolTomSAR experiments
摘要： We experimentally investigate mutual information and generalized mutual information for coherent optical transmission systems. The impact of the assumed channel distribution on the achievable rate is investigated for distributions in up to four dimensions. Single channel and wavelength-division multiplexing (WDM) transmission over transmission links with and without inline dispersion compensation are studied. We show that for conventional WDM systems without inline dispersion compensation, a circularly symmetric complex Gaussian distribution is a good approximation of the channel. For other channels, such as with inline dispersion compensation, this is no longer true and gains in the achievable information rate are obtained by considering more sophisticated four-dimensional (4D) distributions. We also show that for nonlinear channels, gains in the achievable information rate can also be achieved by estimating the mean values of the received constellation in four dimensions. The highest gain for such channels is seen for a 4D correlated Gaussian distribution.
关键词： fiber nonlinear optics,optical fiber communication,digital communication,Channel models,mutual Information
更新于2025-09-19 17:13:59
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Fast wavefront reconstruction method of reception diversity atmospheric laser communication based on Fractional Brownian motion
摘要： In order to reconstruct the wavefront of reception diversity atmospheric laser communication, the paper first improves the reconstructing modal method of traditional single-input-single-output laser communication based on mutual information. Because the distortion wavefront caused by atmospheric turbulence is a fractal surface of Fractional Brownian motion, a fast wavefront reconstructing method of reception diversity atmospheric laser communication is proposed in this paper. The result shows that the wavefront reconstructing method based on mutual information can optimize the reconstructing performance effectively. Besides, the running time of proposed reconstructing method of reception diversity atmospheric laser communication decreases greatly. Through the comparison of generated wavefront and reconstructed wavefront, the feasibility of the proposed method is demonstrated. It proves that the wavefront reconstructed by proposed method based on Fractional Brownian motion can fit the theory well through the comparison of phase structure function.
关键词： Atmospheric turbulence,Wavefront reconstruction,Fractional Brownian motion,Reception diversity,Mutual information
更新于2025-09-16 10:30:52
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Optical Fiber and Wireless Communications || Fundamental Analysis for Visible Light Communication with Input‐Dependent Noise
摘要： Recently, visible light communication (VLC) has drawn much attention. In literature, the noise in VLC is often assumed to be independent of the input signal. This assumption neglects a fundamental issue of VLC: due to the random nature of photon emission in the lighting source, the strength of the noise depends on the signal itself. Therefore, the input-dependent noise in VLC should be considered. Given this, the fundamental analysis for the VLC with input-dependent noise is presented in this chapter. Based on the information theory, the theoretical expression of the mutual information is derived. However, the expression of the mutual information is not in a closed form. Furthermore, the lower bound of the mutual information is derived in a closed form. Moreover, the theoretical expression of the bit error rate is also derived. Numerical results verify the accuracy of the derived theoretical expressions in this chapter.
关键词： bit error rate,input-dependent noise,visible light communication,mutual information
更新于2025-09-12 10:27:22
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Exploratory Visual Analysis of Multispectral EO Images Based on DNN
摘要： Exploratory visual analysis is often required to assist human operator to understand and interpret Earth Observation (EO) images. Optimal image representation offers cognitive support in discovering relevant facts about the scene with respect to a particular application. This is of crucial importance for training data sets selection in all Machine Learning tasks, particularly in the design of active learning tools for multispectral (MS) EO data. This paper proposes a deep neural network (DNN) based method to compress, learn and reveal the most significant information included in the spectral bands of EO data in support of relevant visualization for image content analysis. The advanced method uses a DNN to discover the most suggestive pseudo-color representation able to highlight the entire MS image content better than the particular 3 bands selection (R, G, B). We propose the use of information theory and the concept of mutual information to rank the spectral bands based on the amount of information contained, by applying the minimum-redundancy-maximum-relevance (mRMR) criterion on a the image so that we obtain the ranked bands. A DNN stacked autoencoder based paradigm is developed in order to extract and compress in three bands the overall information from the MS EO data. The developed method is demonstrated and validated for Sentinel 2 dataset.
关键词： mRMR,information theory,mutual information,DNN,EO images
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Discovering Temporal Patterns of Air Quality in Different Parts of Europe with Data Driven Feature Extraction
摘要： Air quality is strongly affecting human lifestyle all over the world, and its impact is apparent on healthcare, sustainable development, welfare and public administration policies. Accurate understanding of the polluting processes requires to analyze huge volumes of records, so that significant patterns and regularities can be detected. In this paper, we introduce a framework to explore the air pollution dynamics over all Europe by means of a data driven feature extraction approach. Taking advantage of MODIS records, we are able to investigate daily trends of air quality from 2003 to 2016. By means of an automatic learning scheme based on mutual information maximization, we extract the most significant patterns in the dataset. Experimental results show that the proposed approach is able to identify relevant air pollution trends that can be associated with specific physical phenomena on ground.
关键词： mutual information maximization,MODIS,data driven feature extraction,Europe,air quality
更新于2025-09-10 09:29:36
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Fusion of noisy images based on joint distribution model in dual-tree complex wavelet domain
摘要： Source images are frequently corrupted by noise before fusion, which will lead to the quality decline of fused image and the inconvenience for subsequent observation. However, at present, most of the traditional medical image fusion scheme cannot be implemented in noisy environment. Besides, the existing fusion methods scarcely make full use of the dependencies between source images. In this research, a novel fusion algorithm based on the statistical properties of wavelet coefficients is proposed, which incorporates fusion and denoising simultaneously. In the proposed algorithm, the new saliency and matching measures are defined by two distributions: the marginal statistical distribution of single wavelet coefficient fit by the generalized Gaussian Distribution and joint distribution of dual source wavelet coefficients modeled by the anisotropic bivariate Laplacian model. Additionally, the bivariate shrinkage is introduced to develop a noise robust fusion method, and a moment-based parameter estimation applied in the fusion scheme is also exploited in denoising method, which allows to achieve the consistency of fusion and denoising. The experiments demonstrate that the proposed algorithm performs very well on both noisy and noise-free images from multimodal medical datasets (computerized tomography, magnetic resonance imaging, magnetic resonance angiography, etc.), outperforming the conventional methods in terms of both fusion quality and noise reduction.
关键词： statistical distribution,KL-divergence,bi-shrinkage,image fusion,mutual information
更新于2025-09-09 09:28:46
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[Smart Innovation, Systems and Technologies] Information Systems and Technologies to Support Learning Volume 111 (Proceedings of EMENA-ISTL 2018) || A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
摘要： Band selection is a great challenging task in the classi?cation of hyperspectral remotely sensed images HSI. This is resulting from its high spectral resolution, the many class outputs and the limited number of training samples. For this purpose, this paper introduces a new ?lter approach for dimension reduction and classi?cation of hyperspectral images using information theoretic (normalized mutual information) and support vector machines SVM. This method consists to select a minimal subset of the most informative and relevant bands from the input datasets for better classi?cation ef?ciency. We applied our proposed algorithm on two well-known benchmark datasets gathered by the NASA’s AVIRIS sensor over Indiana and Salinas valley in USA. The experimental results were assessed based on different evaluation metrics widely used in this area. The comparison with the state of the art methods proves that our method could produce good performance with reduced number of selected bands in a good timing.
关键词： Support vector machines,Classi?cation,Dimension reduction,Band selection,Hyperspectral images,Normalized mutual information
更新于2025-09-09 09:28:46
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Contrast Enhancement Effect on High Dynamic Range Image Registration Using Mutual Information
摘要： Mutual Information (MI) is emerging as a very strong metric for image registration purposes in the literature. It has many applications from remote sensing to medical image registration. From this wide range of use of MI, images are mostly expressed in different numbers of bits (high dynamic range) especially in medical and satellite imaging. In such cases, contrast enhancement becomes inevitable before MI-based image registration since all the images should be in the same intensity range. The change in intensities in images will directly affect MI metric. Contrast enhancement methods also have a significant effect on the registration performance due to MI metric and this problem is not sufficiently addressed in the literature. In this paper, the effect of the outstanding contrast enhancement methods is examined on image registration performance. For this purpose, high dynamic range satellite images were used and Monte Carlo tests were performed. They are tried to be aligned with MI and constrained optimization by linear approximations (COBYLA) optimization algorithm. Consequently, it is found that contrast enhancement methods have an effect on MI-based image registration. It is concluded that Laplacian of Gaussian unsharp blending masks (AHE) and (LoGUnsarp), adaptive histogram equalization (CLAHE) limited adaptive histogram equalization contrast methods have better registration performance. They can be preferred in such registration purposes.
关键词： contrast enhancement,mutual information,optimization,image registration
更新于2025-09-09 09:28:46