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.     

双群交换微粒群算法及在Shearlet图像去噪中的应用

分析基于不同进化模型的双群交换微粒群优化算法的不足,提出改进的双群交换微粒群优化算法。算法将微粒分成大小相同的两分群,第一分群采用标准微粒群模型进化,第二分群采用Cognition Only模型进化,当微粒进化到稳定状态,从第一分群随机抽取部分粒子与第二分群适应值最差粒子进行交换,重复上述操作直到找到最优解。实验结果显示：该算法有更好的全局寻优能力和达优率。为验证算法实用性,将改进算法用于Shearlet图像去噪。该方法根据Shearlet变换域不同尺度和方向系数的分布特性,采用改进算法自适应确定各尺度和方向的最优阈值,实现基于图像内容的自适应去噪。实验表明,该方法能有效滤除图像噪声,较好保留图像边缘信息,去噪后图像具有更高峰值信噪比（PSNR）。     

The Implementation of Optimization Methods for Contrast Enhancement

The performances of the multivariate techniques are directly related to the variable selection process, which is time consuming and requires resources for testing each possible parameter to achieve the best results. Therefore, optimization methods for variable selection process have been proposed in the literature to find the optimal solution in short time by using less system resources. Contrast enhancement is the one of the most important and the parameter dependent image enhancement technique. In this study, two optimization methods are employed for the variable selection for the contrast enhancement technique. Particle swarm optimization (PSO) and artificial bee colony (ABC) optimization methods are implemented to the histogram stretching technique in parameter selection process. The results of the optimized histogram stretching technique are compared with one of the parameter independent contrast enhancement technique; histogram equalization. The results show that the performance of the optimized histogram stretching is better not only in distorted images but also in original images. Histogram equalization degraded the original images while the optimized histogram stretching has no effect due to being an adaptive solution.     

Enhancement of MRI images of hamstring avulsion injury using histogram based techniques

A novel histogram based image enhancement technique is introduced to visualize the image more effectively. The proposed method uses hamstring avulsion injury Magnetic Resonance Imaging (MRI) images from the database. First, the image is clipped using the histogram. Second, the image is subdivided into eight sub-images and enhanced individually until a better enhancement rate is maintained to obtain the final output of the proposed method. The proposed method shows effective enhancement for clear visualization of the injury. The strength of the proposed method is compared with different histogram based enhancement techniques based on the parameters such as F-measure, Contrast improvement index (CII), Absolute Mean Brightness Error (AMBE) and Peak Signal to Noise Ratio (PSNR) to determine the efficient enhancement technique. The parameters are defined to be significant for different enhancement techniques based on the statistical analysis. Further classification of the enhancement techniques are performed with the help of decision tree classifier. Based on the results of the classifier, the proposed algorithm is stated to be more significant and efficient in enhancing the region of interest in the Hamstring Avulsion Injury MRI images. Thus the proposed method shows effective enhancement for improved visualization of the hamstring injury for the diagnosis of the state of injury. With these results, the region of injury can be analysed effectively for further processing.

     

Histogram modification based enhancement along with contrast-changed image quality assessment

Contrast is the difference in visual characteristics which make an object more recognizable. Despite the significance of contrast enhancement (CE) in image processing applications, few attempts have been made on assessment of the contrast change. In this paper, a visual information fidelity-based contrast change metric (VIF-CCM) is presented which includes visual information fidelity (VIF), local entropy, correlation coefficient, and mean intensity measures. The validation results of the presented VIF-CCM show its efficiency and superiority over the state-of–the-arts image quality assessment metrics. A histogram modification based contrast enhancement (HMCE) method is also proposed in this paper. The proposed HMCE comprises of four steps: segmentation of the input image, employing a set of weighting constraints, applying the combination of adaptive gamma correction and equalization on modified histogram, and optimization the value of the constraint weights by PSO algorithm. Experimental results demonstrate that the proposed HMCE outperforms other existing CE methods subjectively and objectively.

     

Adaptive enhancement of underwater images using multi-objective PSO

Underwater images have poor clarity and bad contrast due to low illumination in deep water. Moreover, underwater images are bluish-green in appearance due to inherent wavelength absorption property of water. Therefore, the study of underwater images is a difficult task. Being computationally simple, histogram-based enhancement techniques are obvious choice for improvement of contrast and color of underwater images. However, due to lack of any guidance mechanism, these techniques can overstretch the histogram leading to artifacts in the image. Hence, an adaptive method named ‘Contrast and Information Enhancement of Underwater Images’ (CIEUI) is proposed, which enhances underwater images by improving their contrast and information content using Multi-Objective Particle Swarm Optimization (MOPSO). Objective functions of MOPSO are chosen to act as guiding mechanism to ensure color & contrast correction and information enhancement respectively without introducing artifacts. Computed results not only have good contrast and color performance but also have better information content. The proposed CIEUI technique performs quantitatively and qualitatively better as compared to state-of-the-art algorithms.

     

An effective local contrast enhancement technique by blending of local statistics and Genetic Algorithm

Nowadays, Image enhancement finds enormous image processing applications, which are related to practical situations, Contrast enhancement is one among the different image enhancement techniques that intends to improve the image visibility. Though several works for local contrast enhancement are available in the literature, the effectiveness remains an issue and the enhancement performance needs to be improved. In this paper, a local contrast enhancement technique is proposed for both gray scale images and RGB color images. The proposed technique is comprised of two stages of enhancement, namely, local statistics-based image enhancement and Genetic Algorithm based local contrast enhancement. The former stage is a pre-enhancement stage and the later is the major stage of enhancement. In the former stage, the image is processed in window basis and the local statistics of the image is obtained. Based on the local statistics, the image is enhanced. In the later stage, the window based operation is performed over the preenhanced image and the local contrast is enhanced. The Genetic Algorithm aids in searching of an optimal contrast factor, which plays vital role in the contrast enhancement. The technique is evaluated with both gray scale images as well as RGB color images and performance is compared with the existing contrast enhancement techniques.     

基于模拟多曝光融合的低照度图像增强方法

针对部分低照度图像整体亮度偏暗、对比度差和视觉信息偏弱等问题，提出一种基于模拟多曝光融合的低照度图像增强方法。首先，利用改进的变分Retinex模型和形态学的结合产生基准图来保证曝光图像集中的主体信息；其次，结合Sigmoid函数和伽马矫正构造新的光照补偿归一化函数，同时提出了一种基于高斯引导滤波的反锐化掩模算法，用于调整基准图的细节；最后，分别从亮度、色调和曝光率设计曝光图集的加权值，通过多尺度融合得到最终增强结果，有效地避免了增强结果中的光晕和颜色失真。在不同的公开数据集上的实验结果表明，与传统的低照度图像增强方法进行相比，所提方法降低了亮度失真率，提升了视觉信息保真度。该方法能够有效地保留视觉信息，有利于实现低照度图像增强的实时性应用。     

Contrast Enhancement and Intensity Preservation for Gray-Level Images Using Multiobjective Particle Swarm Optimization

The contrast enhancement of gray-level digital images is considered in this paper. In particular, the mean image intensity is preserved while the contrast is enhanced. This provides better viewing consistence and effectiveness. The contrast enhancement is achieved by maximizing the information content carried in the image via a continuous intensity transform function. The preservation of image intensity is obtained by applying gamma-correction on the images. Since there is always a trade-off between the requirements for the enhancement of contrast and preservation of intensity, an improved multiobjective particle swarm optimization procedure is proposed to resolve this contradiction, making use of its flexible algorithmic structure. The effectiveness of the proposed approach is illustrated by a number of images including the benchmarks and an image sequence captured from a mobile robot in an indoor environment.     

基于非下采样Shearlet变换与模糊对比度的合成孔径雷达图像增强

针对合成孔径雷达（SAR）图像在成像和传输过程中引入噪声和干扰从而导致图像清晰度下降、细节丢失等问题,提出了一种非下采样Shearlet变换（NSST）与模糊对比度的SAR图像增强算法。首先,原始图像经NSST分解成一个低频分量和若干个高频分量;然后对低频分量进行线性增强以提高整体对比度,对高频分量采用阈值法进行增强以去除图像中的噪声;接着对处理后的两部分分量进行NSST反变换得到重构图像;最后采用模糊对比度算法对重构图像进行增强,提高图像细节信息和层次感,得到增强后的图像。对40幅图像的实验结果表明,与直方图均衡化、多尺度Retinex增强算法、基于Shearlet变换和多尺度Retinex的遥感图像增强算法、基于剪切波域改进Gamma校正的医学图像增强算法相比,该算法的图像峰值信噪比至少提升了22.9%,均方根误差至少降低了36.2%,能明显提升图像的清晰度,使图像的纹理信息更加清晰。     

A real-time image processor with combining dynamic contrast ratio enhancement and inverse gamma correction for PDP

Inverse gamma correction must be performed before displaying the received video signal because alternating current plasma display panel (AC PDP) has a linear output luminance response to a digital-valued input. At the same time contrast ratio enhancement is necessary for improving the image quality of display devices. The histogram equalization (HE) is an important contrast ratio enhancement method. But sometimes HE can produce unrealistic effects in images. In this paper, a new method of combining dynamic contrast ratio enhancement and inverse gamma correction for AC PDP is proposed. The dynamic contrast ratio enhancement and the inverse gamma correction are realized simultaneously in the proposed method. Furthermore the over-enhancement caused by the traditional HE can be avoided. A real-time image processor with the proposed method was designed and implemented. Simulations and experimental results on a 50-in. AC PDP show that the image quality of AC PDP can be improved obviously.     

基于直方图均衡化与MSRCR的沙尘降质图像增强算法

针对沙尘天气下图像色彩偏移严重及对比度低等问题,提出一种基于直方图均衡化与带色彩恢复的多尺度视网膜（MSRCR）增强的沙尘降质图像增强算法。通过偏色校正和图像增强两个步骤进行图像恢复,将RGB图像各通道预处理后利用限制对比度自适应直方图均衡方法得到校正后的图像,对图像采用双边滤波进行降噪处理,通过MSRCR算法进一步解决色彩失衡问题。由于处理后的图像对比度较低,存在一定色偏,利用伽马校正和基于图像分析的偏色检测及颜色校正方法进行处理得到最终结果。对大量沙尘降质图像进行仿真实验,结果表明,该算法能够有效处理不同偏色程度的沙尘图像,不仅提高了图像的对比度,而且有效避免了图像颜色偏移现象,相比GCANet、MSRCR等算法,平均时间效率提升了46.2%~94.7%。     

基于PCA和粒子群优化算法的焊点缺陷识别

针对生产线上的表面贴装技术(SMT）焊点图像的特点,提出了一种基于PCA和粒子群算法-误差反向传播（PSO-BP）神经网络的焊点缺陷识别方法。首先使用图像处理技术和CCD传感器对PCB焊点图像进行预处理,采用中值滤波、灰度图像增强、全局阈值法等方法,有效抑制噪声干扰并提高了图像对比度,提取出较好的图像特征。然后运用主成分分析法提取包含焊点86.6%特征信息的5个主成分,并输入到经粒子群算法改进后的BP神经网络。通过具体的实验分析,结果表明改进的BP神经网络具有较好的识别分类效果,能够对正常、多锡、少锡、漏焊四种不同类型的焊点进行识别,准确率达93.22%,算法可靠,在实际生产中能够有效的提高检测效率。     

Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization

In the present paper, particle swarm optimization, a relatively new population based optimization technique, is applied to optimize the multidisciplinary design of a solid propellant launch vehicle. Propulsion, structure, aerodynamic (geometry) and three-degree of freedom trajectory simulation disciplines are used in an appropriate combination and minimum launch weight is considered as an objective function. In order to reduce the high computational cost and improve the performance of particle swarm optimization, an enhancement technique called fitness inheritance is proposed. Firstly, the conducted experiments over a set of benchmark functions demonstrate that the proposed method can preserve the quality of solutions while decreasing the computational cost considerably. Then, a comparison of the proposed algorithm against the original version of particle swarm optimization, sequential quadratic programming, and method of centers carried out over multidisciplinary design optimization of the design problem. The obtained results show a very good performance of the enhancement technique to find the global optimum with considerable decrease in number of function evaluations.     

基于小生境粒子群算法的图像分割方法

为了得到分割图像的最佳阈值,提出了一种基于小生境粒子群算法的图像分割方法。小生境粒子群算法通过划分小生境的方法,保持了物种的多样性,克服了粒子群算法容易陷入局部解,后期收敛速度慢的缺点,提高了算法的全局寻优能力。该方法基于最大类间方差阈值分割技术,用小生境粒子群算法对适应度函数进行优化,得到最佳阈值,并用该阈值对图像进行分割。实验结果表明,与最大类间方差法,基于基本粒子群算法的最大类间方差分割法相比,所提出的方法不仅能得到理想的分割结果,而且分割速度也得到了提高。     

基于视觉相似性的半色调图像评价方法

为衡量半色调图像质量或加网算法的优劣,提出平均亮度相似性误差、平均对比度相似性误差和视觉相似性误差以衡量半色调图像与连续调图像之间的视觉相似性。该方法根据人眼视觉局部适应性特征,把图像划分子区域,利用亮度掩蔽和对比度掩蔽的特征,获得各子区域的亮度相似性误差和对比度相似性误差,最终获得平均亮度相似性误差、平均对比度相似性误差和视觉相似性误差分别评价半色调图像与原连续调图像在亮度和纹理的视觉相似性,及半色调图像中的局部缺陷。分别通过与峰值信噪比(PSNR)和权重信噪比(WSNR)在亮度相似性上进行比较,与全局质量因子(UQI)和图像结构相似(SSIM)在评价纹理相似性上进行比较,发现所提质量评价方法比较接近人眼视觉评价的结果。     

An approach to multimodal biomedical image registration utilizing particle swarm optimization

Biomedical image registration, or geometric alignment of two-dimensional and/or three-dimensional (3D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity metric between the images. Local optimization techniques frequently fail because functions of these metrics with respect to transformation parameters are generally nonconvex and irregular and, therefore, global methods are often required. In this paper, a new evolutionary approach, particle swarm optimization, is adapted for single-slice 3D-to-3D biomedical image registration. A new hybrid particle swarm technique is proposed that incorporates initial user guidance. Multimodal registrations with initial orientations far from the ground truth were performed on three volumes from different modalities. Results of optimizing the normalized mutual information similarity metric were compared with various evolutionary strategies. The hybrid particle swarm technique produced more accurate registrations than the evolutionary strategies in many cases, with comparable convergence. These results demonstrate that particle swarm approaches, along with evolutionary techniques and local methods, are useful in image registration, and emphasize the need for hybrid approaches for difficult registration problems.     

变压器油下图像增强算法

为解决变压器检测机器人在变质、变色的变压器油内部采集的图像存在色彩失真、对比度低等问题,提出一种变压器油下图像融合增强算法.首先,利用完美反射算法对图像进行白平衡处理,以消除油下光照强度不均匀对图像颜色的影响,使得色彩更加均衡;然后,对色彩校正的图像进行自适应伽马校正,以提高图像的对比度;最后,采用多尺度融合策略将色彩校正后的图像与自适应伽马校正处理后的图像进行融合,得到变压器油下清晰的图像.实验结果表明,经所提出算法处理后的变压器油下图像色彩鲜明、细节丰富,与原始图像相比,图像质量评价指标(UCIQE)、特征点匹配个数以及信息熵均有显著提高,能够为变压器内部故障检测提供清晰的数据.     

Unsupervised classification of polarimetric SAR image with dynamic clustering: An image processing approach

This paper proposes a new unsupervised classification approach for automatic analysis of polarimetric synthetic aperture radar (SAR) image. Classification of the information in multi-dimensional polarimetric SAR data space by dynamic clustering is addressed as an optimization problem and two recently proposed techniques based on particle swarm optimization (PSO) are applied to find optimal (number of) clusters in a given input data space, distance metric and a proper validity index function. The first technique, so-called multi-dimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem, fractional global best formation (FGBF) technique is then presented, which basically collects all promising dimensional components and fractionally creates an artificial global-best particle (aGB) that has the potential to be a better “guide” than the PSO’s native gbest particle. In this study, the proposed dynamic clustering process based on MD-PSO and FGBF techniques is applied to automatically classify the color-coded representations of the polarimetric SAR information (i.e. the type of scattering, backscattering power) extracted by means of the Pauli or the Cloude–Pottier decomposition algorithms. The performance of the proposed method is evaluated based on fully polarimetric SAR data of the San Francisco Bay acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band. The proposed unsupervised technique determines the number of classes within polarimetric SAR image for optimal classification performance while preserving spatial resolution and textural information in the classified results. Additionally, it is possible to further apply the proposed dynamic clustering technique to higher dimensional (N-D) feature spaces of fully polarimetric SAR data.     

红外图像自适应增强的模糊粒子群优化算法

针对红外图像目标与背景区分不明显、对比度低的特点,把粒子群优化算法应用到红外图像增强中,提出了红外图像自适应增强的模糊粒子群优化算法。灰度变换增强是红外图像增强的首选方法之一,而选取适当的阈值是其取得良好的增强效果的有力保证。该算法通过粒子群优化算法来寻求最大熵准则下的自适应阈值,然后用模糊灰度变换增强方法自适应地拉伸红外图像灰度,增强图像。仿真实验表明,相对于常见的直方图处理,该算法能降低红外图像中背景对目标的影响,能提高红外图像的对比度。