免疫粒子群优化算法在图像融合中的应用

提出了一种基于图像分块的小波多聚焦图像融合方法,并将免疫粒子群优化搜索策略应用于多聚焦图像融合子块寻优中。将图像子块作为粒子,以寻求最优组合分块形成的融合图像。利用两种评价参量,即信息熵和交叉熵进行不同图像融合方法的分析及效果评价,实验结果表明,其融合性能优于对图像只进行分块而不作小波分解的融合方法和只作小波分解而不进行分块的融合方法,该方法既能消除块痕迹,又能节约运算量,取得了很好的融合效果。与标准粒子群相比,免疫粒子群的收敛性能和达优率更好。     

Fault-tolerant image filter design using particle swarm optimization

This article describes a mixed constrained image filter design with fault tolerance using particle swarm optimization (PSO) on a reconfigurable processing array. There may be some faulty configurable logic blocks (CLBs) in a reconfigurable processing array. The proposed method with PSO autonomously synthesizes a filter fitted to the reconfigurable device with some faults in order to optimize the complexity and power of the circuit, and the signal delay in both the CLBs and the wires. An image filter for noise reduction is experimentally synthesized to verify the validity of our method. By evolution, the quality of the optimized image filter on a reconfigurable device with a few faults is almost same as that on a device with no faults.     

基于粒子群优化的多特征融合的商标图像检索

提出一种基于粒子群优化的多特征融合的商标图像检索方法,该方法可自动优化多特征融合的权重,提高图像检索系统的自适应性,解决了多特征商标图像检索中的权重分配问题。在1 000幅图像构成的商标图像库进行检索实验,实验结果表明,与基于单一特征的检索方法和一些多特征融合的检索方法相比,提出方法的检索性能最优。     

Mixed constrained image filter design using particle swarm optimization

This article describes an evolutionary image filter design for noise reduction using particle swarm optimization (PSO), where mixed constraints on the circuit complexity, power, and signal delay are optimized. First, the evaluated values of correctness, complexity, power, and signal delay are introduced to the fitness function. Then PSO autonomously synthesizes a filter. To verify the validity of our method, an image filter for noise reduction was synthesized. The performance of the resultant filter by PSO was similar to that of a genetic algorithm (GA), but the running time of PSO is 10% shorter than that of GA.     

Simultaneous image color correction and enhancement using particle swarm optimization

Color images captured under various environments are often not ready to deliver the desired quality due to adverse effects caused by uncontrollable illumination settings. In particular, when the illuminate color is not known a priori, the colors of the objects may not be faithfully reproduced and thus impose difficulties in subsequent image processing operations. Color correction thus becomes a very important pre-processing procedure where the goal is to produce an image as if it is captured under uniform chromatic illumination. On the other hand, conventional color correction algorithms using linear gain adjustments focus only on color manipulations and may not convey the maximum information contained in the image. This challenge can be posed as a multi-objective optimization problem that simultaneously corrects the undesirable effect of illumination color cast while recovering the information conveyed from the scene. A variation of the particle swarm optimization algorithm is further developed in the multi-objective optimization perspective that results in a solution achieving a desirable color balance and an adequate delivery of information. Experiments are conducted using a collection of color images of natural objects that were captured under different lighting conditions. Results have shown that the proposed method is capable of delivering images with higher quality.     

The Retinex-based image dehazing using a particle swarm optimization method

To the best of our knowledge, currently the physical model based method is still an ill posed problem. Additionally, the image enhancement approaches also suffer from the texture preservation issue. Retinex-based approach is proved its effectiveness in image dehazing while the parameter should be turned properly. Therefore, in this paper, the particle swarm optimization (PSO) algorithm is firstly performed to optimize the parameter and the hazed image is converted into hue, saturation, intensity(HSI) for color compensation, In the other hand, the multi-scale local detail upgrading and the bilateral filtering approaches are designed to overcome the dehazing artefacts and edge preservation, which could further improve the overall visual effect of images. Experimental results on natural and synthetic images by using qualitative analysis and frequently used quantitative evaluation metrics illustrate the approving defogging effect of the proposed method. For instance, in a natural image road, our method achieves the higher e for 0.63, γ for 3.21 and H for 7.81, respectively and lower σ for 0.04. In a synthetic image poster, the higher PSNR for 18.17 and SSIM for 0.78 are also acquired compared to other explored approaches in this paper. Besides, the results performed on other underwater and aerial images in this study further demonstrates its defog effectiveness.

     

Dynamic clustering using particle swarm optimization with application in image segmentation

A new dynamic clustering approach (DCPSO), based on particle swarm optimization, is proposed. This approach is applied to image segmentation. The proposed approach automatically determines the “optimum” number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions. Using binary particle swarm optimization the “best” number of clusters is selected. The centers of the chosen clusters is then refined via the K-means clustering algorithm. The proposed approach was applied on both synthetic and natural images. The experiments conducted show that the proposed approach generally found the “optimum” number of clusters on the tested images. A genetic algorithm and random search version of dynamic clustering is presented and compared to the particle swarm version.     

Rotation-invariant texture image retrieval using particle swarm optimization and support vector regression

This paper presents a novel rotation-invariant texture image retrieval using particle swarm optimization (PSO) and support vector regression (SVR), which is called the RTIRPS method. It respectively employs log-polar mapping (LPM) combined with fast Fourier transformation (FFT), Gabor filter, and Zernike moment to extract three kinds of rotation-invariant features from gray-level images. Subsequently, the PSO algorithm is utilized to optimize the RTIRPS method. Experimental results demonstrate that the RTIRPS method can achieve satisfying results and outperform the existing well-known rotation-invariant image retrieval methods under considerations here. Also, in order to reduce calculation complexity for image feature matching, the RTIRPS method employs the SVR to construct an efficient scheme for the image retrieval.     

Robust optimization using multi-objective particle swarm optimization

This article proposes an algorithm to search for solutions which are robust against small perturbations in design variables. The proposed algorithm formulates robust optimization as a bi-objective optimization problem, and fi nds solutions by multi-objective particle swarm optimization (MOPSO). Experimental results have shown that MOPSO has a better performance at fi nding multiple robust solutions than a previous method using a multi-objective genetic algorithm.     

多策略融合的改进粒子群优化算法

为解决传统粒子群算法收敛精度低、收敛速度慢和易陷入局部最优的问题,提出了一种多策略融合的改进粒子群算法。首先,设计了一种基于中垂线算法的游离粒子位置更新方法,加快了游离粒子的收敛速度;其次,设计了一种在最优粒子附近生成爆炸粒子的策略,以增强算法的寻优精度和寻优速度,为适应前两个策略,还设计了一种仅依靠全局最优粒子位置的粒子速度更新策略;最后,将基于概率分层的简化粒子群优化算法的惯性权重和粒子位置更新方法用于本算法。与其他五种改进粒子群算法进行了对比实验,结果表明提出的改进算法无论是处理低维问题还是高维问题表现均具有较大优势,性能更优越。     

多目标粒子群优化PCNN参数的图像融合算法

目的 脉冲耦合神经网络（PCNN）在图像融合上往往因为参数设置问题而达不到最佳效果,为了提高图像融合的质量,提出了一种基于多目标粒子群优化PCNN参数的图像融合算法。方法 首先用多目标粒子群对PCNN模型参数进行优化得到最优PCNN参数模型,然后利用双复树小波（DTCWT）对图像多尺度分解,将高频分量通过优化好的PCNN模型进行高频融合,低频分量通过拉普拉斯分量绝对和（SML）来进行低频融合,最后通过DTCWT逆变换实现图像的融合。结果 分别与DTCWT,拉普拉斯金字塔变换（LP）以及非下采样Contourlet变换（NSCT）进行实验对比,融合图像Clock,Lab的融合结果在客观指标上的互信息（8.062 3,7.908 5）、图像的品质因数（0.716 2,0.714 2）和标准差（51.213,47.671）都优于其他方法,熵和其他方法差不多,融合结果能够获得更好的视觉效果以及较大的互信息值和边缘信息保留值。结论 该方法有较好融合图像的能力,可适用于计算机视觉、医学、遥感等领域。     

基于量子粒子群算法的医学图像配准

医学图像配准是图像融合等图像处理需要先行解决的问题.首先用坎尼算子提取图像的边缘,再用K均值聚类算法进行聚类分析提取轮廓特征点,然后引入了带有量子行为的粒子群优化算法来求解配准所需的空间变换参数.实验结果表明,QPSO能够迅速地在全局范围内找到最优解,应用于多模态医学图像配准是可行的.     

基于粒子群优化的图像自适应增强方法

将免疫粒子群优化算法和非完全Beta函数结合,提出了一种自适应图像对比度增强方法.该免疫粒子群优化算法结合了粒子群优化算法具有的全局寻优能力和免疫系统的免疫信息处理机制,改善了粒子群优化算法摆脱局部极值点的能力.利用免疫粒子群优化算法自动搜索最佳的灰度变换参数,从而获得一条最佳的灰度变换曲线,实现对图像进行全局增强处理.实验结果表明,该算法不仅能有效地提高图像整体对比度和视觉效果,而且适合图像的自动化处理.     

An image contrast enhancement algorithm for grayscale images using particle swarm optimization

This paper addresses a contrast enhancement technique that combines classical contrast enhancement with an evolutionary approach. The central goal of this work is to increase the information content and enhance the details of an image using an adaptive gamma correction technique aided by particle swarm optimization. Gamma correction is a well established technique that preserves the mean brightness of an image that produces natural looking images by the choice of an optimal gamma value. Here, Swarm intelligence based particle swarm optimization is employed to estimate an optimal gamma value. In the proposed method, the edge and information content (entropy) are the parameters used to formulate the fitness function. The proposed method is compared with state-of-the-art of techniques in terms of Weighted Average Peak Signal to Noise Ratio (WPSNR), Contrast, Homogeneity, Contrast Noise Ratio (CNR), and Measure of Enhancement (EME). Simulation results demonstrate that the proposed particle swarm optimization based contrast enhancement method improves the overall image contrast and enriches the information present in the image. In comparison to other contrast enhancement techniques, the proposed method brings out the hidden details of an image and is more suitable for applications in satellite imaging and night vision.     

一种求解多峰函数优化问题的量子行为粒子群算法

介绍了一种利用量子行为粒子群算法（QPSO）求解多峰函数优化问题的方法。为此,在QPSO中引进一种物种形成策略,该方法根据群体微粒的相似度并行地分成子群体。每个子群体是围绕一个群体种子而建立的。对每个子群体通过QPSO算法进行最优搜索,从而保证每个峰值都有同等机会被找到,因此该方法具有良好的局部寻优特性。将基于物种形成的QPSO算法与粒子群算法(PSO)对多峰优化问题的结果进行比较。对几个重要的测试函数进行仿真实验结果证明,基于物种形成的QPSO算法可以尽可能多地找到峰值点,峰值收敛性能优于PSO。     

群活性与粒子群优化的稳定性分析

在探讨粒子轨迹的随机过程的基础上,用根轨迹特征值的谱半径来描述粒子群优化的PSO动态系统的稳定性区域;提出并结合实例用群活性刻画了PSO稳定区域中不同参数区间上群行为的动态特征,利用不动点技术通过数值实验描绘出PSO群活性谱及性能图,解释了先前一些文献上提出的典型参数集之所以能够取得满意性能的理由,利用PSO稳定三角中线提出保证PSO收敛性能的参数设置指导策略.     

Toward robust logo watermarking using multiresolution image fusion principles

This paper presents a novel robust watermarking approach called FuseMark based on the principles of image fusion for copy protection or robust tagging applications. We consider the problem of logo watermarking in still images and employ multiresolution data fusion principles for watermark embedding and extraction. A human visual system model based on contrast sensitivity is incorporated to hide a higher energy hidden logo in salient image components. Watermark extraction involves both characterization of attacks and logo estimation using a rake-like receiver. Statistical analysis demonstrates how our extraction approach can be used for watermark detection applications to decrease the problem of false negative detection without increasing the false positive detection rate. Simulation results verify theoretical observations and demonstrate the practical performance of FuseMark.     

Multispectral images fusion by a joint multidirectional and multiresolution representation

This paper proposes a new fusion method that permits an adequate selection of information extracted from source images to obtain fused images with good spatial and spectral quality simultaneously. This method is based on a joint multiresolution multidirectional representation of the source images using a single directional spatial frequency low pass filter bank of low computational complexity, defined in the Fourier domain. The source images correspond to those captured by the IKONOS satellite (panchromatic and multispectral). The results obtained indicate that the proposed method provides, in a simple manner, objective control over the trade‐off between high spatial and spectral quality of the fused images.     

多阈值图像分割的模糊粒子群优化算法

把粒子群算法应用到多阈值图像分割中,结合已有的模糊C-均值聚类法提出了一种基于模糊技术的粒子群优化多阈值图像分割算法。FCM聚类算法是一种局部搜索算法,对初始值较为敏感,容易陷入局部极小值而不能得到全局最优解。PSO算法是一种基于群体的具有全局寻优能力的优化方法。将FCM聚类算法和PSO算法结合起来,将FCM聚类算法的聚类准则函数作为PSO算法中的粒子适应度函数。仿真实验表明新算法在最大熵评判准则下能够得到最优阈值。