基于先验信息和正则化技术的图像复原算法的研究

在湍流退化图像复原研究中,为了消除大气湍流的影响,提出了一种基于先验信息和正则化技术的盲解卷积图像复原算法.该算法是以极大似然估计为基本原理,将目标图像和点扩展函数的先验信息以惩罚项的形式引入到极大似然函数中,同时利用正则化技术优化目标图像和点扩展函数的估计过程,以增加极大似然估计算法的收敛性和稳定性.通过退化图像的复原实验结果表明,该算法在退化模型完全未知的情况下,可以有效的实现对湍流退化图像的盲复原.     

基于飞行参数约束的湍流退化图像快速复原算法

为了克服盲目去卷积求解点扩展函数中由于点扩展函数大小的不确定性引起的复原图像的模糊和振铃现象,提出了一种基于飞行参数估计点扩展函数大小的湍流退化图像快速复原算法.利用飞行参数中飞行马赫数、高度、攻角等已知的先验条件来估计,最扩展函数大小,并建立了一个点扩展函数大小与飞行参数的函数表达式,解决了点扩展函数估计的盲目性,减少了估计时间,提高了退化图像复原校正的质量与速度.仿真试验结果表明:该方法对提高图像复原的速度与质量有明显效果.     

非均匀大气中单幅退化图像复原

利用大气非均匀路径中的图像退化光学模型建立了一种非均匀大气中退化图像复原的新方法.首先估计得到图像中各目标倾斜方向对应的观测处天空背景亮度,然后根据暗原色先验知识估计出图像中场景目标的粗糙相对距离,重点得到了退化模型中反映视线路径非均匀性的比例因子项,进而得到了成像路径中的大气透过率,最后利用得到的这些参量通过非均匀路径退化模型的逆过程对降质图像进行复原.实验结果表明,复原算法对实际采集得到的户外自然退化图像复原效果较好.     

水下数字图像盲复原算法研究

图像复原的目的是从观测到的退化图像重建原始图像,它是图像处理、模式识别、机器视觉等的基础。盲复原作为其中一个重要分支,其主要思想是在点扩展函数未知的情况下,力求获得最佳的清晰效果。由于水下图像退化模型中点扩展函数一般为高斯模型,故针对此提出误差一参数估计法,根据图像退化过程,给出频率域的误差形式,并选定参数的变化范围,再利用复原算法做出误差参数曲线,由此估计出点扩展函数的参数值,最后利用经典的复原算法,如维纳滤波对退化图像进行复原。实验证明该方法获得了比较清楚的复原效果。     

LED夜视探测及图像质量提升

夜视目标探测现已广泛应用于工业、交通、海洋探测、视频监控等多个领域,采用LED阵列为光源,降低成本的同时,也保证了发光强度。然而,由于夜视成像受大气湍流、吸收和散射等环境的影响以及成像设备的限制,图像质量受到严重的退化影响。因此,针对LED夜视,通过量化LED阵列光束在夜间大气中的传输,及大气湍流和气溶胶对成像的影响,计算得到成像过程的调制传递函数(MTF),以传输退化模型的点扩散函数作为先验知识,结合半盲图像复原法,对目标图像进行处理,提高LED夜视成像的图像质量。并将该方法与传统盲复原方法进行比较,验证了该方法的有效性,图像质量得到了明显改善。     

基于噪声分析和稀疏正则化的图像盲复原方法

在海事搜救过程中,机载红外相机拍摄的红外图像由于直升机振动、气流扰动、高速飞行以及红外相机摆扫等因素,严重影响图像质量.根据直升机载红外相机成像特点,提出了一种基于噪声分析和稀疏正则化的图像盲复原方法.该方法首先分析了成像过程中的噪声分布,并对噪声进行预处理,再根据稀疏表达理论,用图像边缘的稀疏先验信息指导点扩散函数复原,接着通过非盲复原方法得到目标图像,将目标图像作为下一次迭代的输入图像,如此循环迭代得到清晰图像.最后,对仿真模糊图像和实拍模糊图像进行了复原实验.实验结果表明这种方法能有效改善图像质量,并且在处理实拍运动模糊图像时,相比其他复原方法效果更好.     

复杂背景红外湍流退化图像复原算法研究

针对大气湍流复杂背景红外退化图像的复原问题,构造了各向异性权重差分项,在此基础上将基于二阶差分的2范数极小作为空间相关性约束应用在复杂背景湍流退化图像相邻两帧点扩展函数的优化求解过程中,提出了针对复杂背景的湍流退化图像复原算法。在微机上进行了一系列复原和方法对比实验,实验结果表明本文方法有效,效果较好。     

长曝光大气湍流退化图像点扩散函数估计

大气湍流能明显降低光学系统的成像质量,距离目标越远,曝光时间越长,受大气扰动越严重,图像越模糊。利用大气湍流退化点扩散函数可以对模糊图像进行复原,但实际自然条件下的点扩散函数往往难以准确获得。结合课题研究背景,针对长曝光大气湍流退化图像复原提出了近似等腰三角形模型,通过该模型能得到准确的大气湍流点扩散函数,并采用维纳滤波获得清晰复原图像。实验表明该方法能够对大视场、远距离条件下获得的长曝光大气湍流退化自然图像估计出准确的点扩散函数,复原图像拥有较好的视觉效果,通过计算灰度平均梯度值和拉普拉斯梯度模两个客观评价标准,进一步证实了该算法的有效性。     

基于各向异性规整化的总变分盲复原算法研究

针对大气湍流退化图像复原问题，提出了一种基于各向异性和非线性规整化的总变分盲复原新算法，该算法主要结合图像和湍流点扩展函数的一些性质采用基于各向异性的空间自适应规整化处理，建立了具有非线性和空间各向异性的规整化函数，使其在恢复目标图像和估计点扩展函数时能自适应地进行梯度平滑。最后，通过交替最小化方案来极小化代价函数和通过定点迭代策略将非线性方程进行线性化处理，快速地估计点扩展函数和恢复图像。在微机上对数字模拟和实际退化图像进行了一系列恢复实验，验证了算法的有效性和稳健性。     

基于APEX方法的改进图像复原算法

在湍流退化图像复原研究中,提出了一种基于APEX方法的改进图像复原算法.该算法采用APEX方法的基本原理,结合真实湍流退化图像的频谱信息特征, 对APEX方法中点扩展函数的估计过程进行了相应的改进,采用多方向的综合估计代替原有一次性估计,从而减少了点扩展函数的估计误差,增加了APEX方法的稳定性.对改进算法和原有算法进行了对比性实验研究,其结果表明,该算法对湍流退化图像的复原较原有算法在稳定性上有一定的提高,减少了随机噪声对图像复原的影响.     

Blind restoration of turbulence degraded images based on two-channel alternating minimization algorithm

Due to the atmospheric turbulence and the system noise, images are blurred in the astronomical or space object detection. Wavefront aberrations and system noise make the capability of detecting objects decrease greatly. A two-channel image restoration method based on alternating minimization is proposed to restore the turbulence degraded images. The images at different times are regarded as separate channels, then the object and the point spread function (PSF) are reconstructed in an alternative way. There are two optimization parameters in the algorithm:the object and the PSF. Each optimization step is transformed into a constraint problem by variable splitting and processed by the augmented Lagrangian method. The results of simulation and actual experiment verify that the two-channel image restoration method can always converge rapidly within five iterations, and values of normalized root mean square error (NRMSE) remain below 3% after five iterations. Standard deviation data show that optimized alternating minimization (OAM) has strong stability and adaptability to different turbulent levels and noise levels. Restored images are approximate to the ideal imaging by visual assessment, even though atmospheric turbulence and systemnoise have a strong impact on imaging. Additionally, the method can remove noise effectively during the process of image restoration.     

Analysis of Linear Distortion Characteristics in Problems of Restoration of Multispectral Images

Available methods for restoration of degraded multispectral images often use the linear distortion model. These distortions can be due to technical characteristics of imaging systems, environmental conditions of signal detection, and motion of the recording camera or the object. In order to efficiently restore a degraded image, it is necessary to analyze the distorting operator. The analysis includes both the way of determining the distortion type and estimation of the operator parameters. In this paper, a method for the analysis of the linear distorting operator by the spectrum of the degraded image is proposed. Using computer simulation, specific features of distortions in the spectra of test multispectral images caused by the impact of typical linear operators are illustrated.     

机场跑道图像去雾增强算法及其实现

机场跑道场景下,由于雾、霾等恶劣天气以及大气散射的作用,使拍摄的图像对比度降低,颜色退化,物体特征难以辨认,影响机场跑道图像的异物检测与识别,带来一些安全隐患。针对上述问题,提出基于双线性插值和中值滤波的改进的引导滤波算法,提高图像对比度和处理速度,利于机场跑道异物检测与识别。仿真结果表明,改进的引导滤波算法与已有算法相比,处理速度提高了2~3倍,且图像彩色值分布更广,直方图更均匀。该算法拥有良好的边缘保持性,在细节增强方面有很好的指导意义。随后将改进的引导滤波算法移植到数字信号处理器( DSP)平台上,以满足实际工程应用对小型化的需求。     

折/反射全方位视觉空间移变点扩散函数建模与图像复原

在分析折/反射全方位视觉(COV)图像模糊尺度分布规律的基础上提出了一种基于径向边缘检测的空间移变点扩散函数(SV-PSF)建模方法,并且研究了基于一种可以方便地加入空域可变模糊模型等先验知识的凸集投影(POCS)图像复原方法。本文提出的建模方法在有限的径向边界区域建立COV的全局SV-PSF模型,避免了一般建模方法需要大量的边缘检测和图像分割计算;采用基于POCS算法空域图像复原方法,很好容纳了SV-PSF这一先验知识,在实验中与MATLAB的盲反卷积工具箱函数的处理结果进行了对比,结果表明本文方法的灵活性和有效性。     

维纳滤波在退化图像恢复中的应用研究

图像由于受到如模糊、失真、噪声等的影响,会造成图像质量的下降,形成退化的数字图像.退化的数字图像会造成图像中的目标很难识别或者图像中的特征无法提取,必须对其进行恢复.维纳波是一种常见的图像复原方法,该方法的思想是使复原的图像与原图像的均方误差最小原则采复原图像.本文进行了对退化图像进行图像复原的仿真实验,在退化图像中加...     

Optimization and regularization of complex task decomposition for blind removal of multi-factor degradation

Most existing image restoration methods based on deep neural networks are developed for images which only degraded by a single degradation mode and imaging under an ideal condition. They cannot be directly used to restore the images degraded by multi-factor coupling. A complex task decomposition regularization optimization strategy (TDROS) is proposed to solve the problem. The restoration of images degraded by multi-factor coupling is a complex task that can be solved by separating these multiple factors, that is, breaking the complex task into numbers of simpler tasks to make the entire complex problem be overcome more easily. Motivated by this idea, the TDROS decomposes the complex task of image restoration into two sub-task: the potential task constrained by regularization and the main task for reconstructing high-definition images. In TDROS, the front of the neural network is focused on the restoration of images degraded by additive noise, while the other part of the network is focused mainly on the restoration of images degraded by blur. We applied the TDROS to an 11-layer convolutional neural network (CNN) and compared it with initial CNNs from the aspects of restoration accuracy and generalization ability. Based on these results, we used TDROS to design a novel network model for the restoration of atmospheric turbulence-degraded images. The experimental results demonstrate that the proposed TDROS can improve the generalization ability of the existing network more effectively than current popular methods, offering a better solution for the problem of severely degraded image restoration. Moreover, the TDROS concept provides a flexible framework for low-level visual complex tasks and can be easily incorporated into existing CNNs.     

Stochastic modeling and estimation of multispectral image data

Multispectral images consist of multiple channels, each containing data acquired from a different band within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. Due to often harsh imaging environments, the acquired data may be degraded by both blur and noise. Simply applying a monochromatic restoration algorithm to each frequency band ignores the cross-channel correlation present within a multispectral image. A Gibbs prior is proposed for multispectral data modeled as a Markov random field, containing both spatial and spectral cliques. Spatial components of the model use a nonlinear operator to preserve discontinuities within each frequency band, while spectral components incorporate nonstationary cross-channel correlations. The multispectral model is used in a Bayesian algorithm for the restoration of color images, in which the resulting nonlinear estimates are shown to be quantitatively and visually superior to linear estimates generated by multichannel Wiener and least squares restoration.     

非清晰区域抑制下的显著对象检测方法

基于上下文感知的显著区域检测模型（Context-Aware,CA）对于大目标和复杂背景图像中显著对象检测存在检测内容缺失和误检的问题.在CA模型的基础上,引入图像清晰度的视觉反差特性,提出非清晰区域抑制下的图像显著对象检测方法.该方法以离散度作为判断图像中是否存在清晰度差异的标准,并对存在差异的图像进行抑制.实验结果表明,非清晰区域抑制的CA方法可以在较好的解决大目标检测和复杂背景误检问题,提高了显著对象检测精度.