Calculation of regularization parameter in the problem of blur removal in digital image

The use of special digital image processing for removal of the blur which results quite often from a mutual relative motion of the optical sensor and object during the exposure is considered. The problem of blur removal from a digital image belongs to the class of incorrect problems, which means that it is impossible to obtain an exact solution resistant to small perturbations in the input data. Therefore, special methods should be invoked. The use of the Tikhonov regularization methods for solving the problem of blur removal from a digital image is demonstrated.     

Addressing image and Poisson noise deconvolution problem using deep learning approaches

Digital images are more important in numerous contemporary applications, and the need for images in the technical field is also increasing drastically. It is used to recognize signatures and faces in many industries and is applicable for intelligent departments. The images are usually associated with the noise content; this may happen due to the instrument imperfections, troubleshooting while collecting data from the acquisition process, and another natural phenomenon. Poisson noise, also known as photon noise, is caused in the images due to the statistical essence of electromagnetic waves. X-ray, visible light, and gamma rays are electromagnetic waves. The enhancement of the convolution model in addressing images is challenging due to the various constituents such as optical aberrations, noise level, and optical setup. The modeling configuration of the image is attained using the point spread function (PSF), which is responsible for the system's impulse response. The quality image is retrieved by denoising and super-resolution (SR) methods; these methods simultaneously eliminate the noise content from the images. A Richardson–Lucy and alternating direction method of multipliers type of non-blind iterative algorithmic approaches associated with the PSF performance in addressing image is comparatively analyzed. The deep learning approach, convolutional neural networks (CNNs), is also employed to understand the nonlinear mapping relationship between the observed data and ground reality. The performance of the various network approaches is compared in this article. The result obtained shows that the deep learning CNNs achieved higher accuracy in producing denoising images. The goal of the proposed system model is to remove the interference noise in images. The high-resolution images are obtained by implementing a SR-based CNN model.     

Identifiability of the stochastic semi-blind deconvolution problem for a class of time-invariant linear systems

Semi-blind deconvolution is the process of estimating the unknown input of a linear system, starting from output data, when the kernel of the system contains unknown parameters. In this paper, identifiability issues related to such a problem are investigated. In particular, we consider time-invariant linear models whose impulse response is given by a sum of exponentials and assume that smoothness is the sole available a priori information on the unknown signal. We state the semi-blind deconvolution problem in a Bayesian setting where prior knowledge on the smoothness of the unknown function is mathematically formalized by describing the system input as a Brownian motion. This leads to a Tychonov-type estimator containing unknown smoothness and system parameters which we estimate by maximizing their marginal likelihood/posterior. The mathematical structure of this estimator is studied in the ideal situation of output data noiseless with their number tending to infinity. Simulated case studies are used to illustrate the practical implications of the theoretical findings in system modeling. Finally, we show how semi-blind deconvolution can be improved by proposing a new prior for signals that are initially highly nonstationary but then become, as time progresses, more regular.     

The deconvolution problem: Fast algorithms including the preconditioned conjugate-gradient to compute a MAP estimator

Different aspects of the deconvolution problem are discussed and a solution by the MAP estimator using the a priori knowledge of smoothness and nonnegativity of the unknown signal is proposed. Several fast algorithms are examined and the conjugate-gradient (CG) method is show to have several advantages: it allows computation of a constrained solution and when used with a preconditioning technique it can be faster than other algorithms for general near-to-Toeplitz systems, in particular, for Toeplitz-block-Toeplitz systems.     

运动模糊情况下障碍物检测预处理

针对在非结构化环境下,摄像机和拍摄目标相对运动造成的图像模糊情况,进行障碍物检测预处理。利用边缘锐度法判定获得的图像是否为运动模糊图像,如果是则利用傅里叶频谱和Radon变换得到点扩展函数后,利用 Lucy-Richardson法进行复原;为了降低后期障碍物检测的处理复杂度,利用提出的一种改进二维熵分割算法进行障碍物粗定位,面积阈值消除噪声影响,完成障碍物检测预处理。通过仿真实验验证了该方法的可行性和有效性,为智能汽车在非结构化环境下避障提供参考依据,为障碍物识别奠定基础。     

A negative dual rectangle cancellation algorithm for the linear assignment problem

In this paper, we consider three alternative primal models and their corresponding alternative dual models for the linear assignment problem. We then define the concept of Negative Dual Rectangle (NDR) and suggest an algorithm that solves two of these dual problems by repeatedly finding and cancelling NDRs until it yields an optimal solution to the assignment problem. The algorithm is simple, flexible, efficient, and unified. We also introduce the notion of partial zero cover as an interpretation of an NDR. We then introduce some heuristic methods for finding NDRs. We also state and prove a lemma to establish the optimal use of an NDR. Furthermore, we show that on a new class of benchmark instances that is introduced in this paper the running time of our algorithm is highly superior to a well-known pure shortest path algorithm.     

考虑装卸的柔性作业车间双资源调度问题

针对"研产混线"中各类制造资源利用率低、非加工时间过长、调度难度大的问题,以生产中最紧缺的夹具资源为例,提出考虑装卸的柔性作业车间双资源调度问题.首先,以最小化完工时间和准结时间为目标建立该问题的数学优化模型;然后,设计快速非支配排序遗传算法对问题进行求解,根据问题特性综合考虑两个目标并设计降准解码算法,随机从交叉算子池和变异算子池中选择算子进行操作,根据非支配等级和拥挤度选择进入下一代的个体;最后,通过数值实验表明,针对考虑装卸的柔性作业车间双资源调度问题,所提出算法能够有效求解该问题,保证完工时间的同时降低准结时间.     

On the dual of a mixed H 2/l 1 optimisation problem

The general discrete-time Single-Input Single-Output (SISO) mixed H2/l1 control problem is considered in this paper. It is found that the existing results of duality theory cannot be directly applied to this infinite dimension optimisation problem. By means of two finite dimension approximate problems, to which duality theory can be applied, the dual of the mixed H2/l1 control problem is verified to be the limit of the duals of these two approximate problems.     

Novel dual discounting functions for the Internet shopping optimization problem: new algorithms

One of the very important topics in discrete optimization, motivated by practical applications, is Internet shopping, which is becoming increasingly popular each year. More classical versions of the Internet shopping optimization problem (ISOP) are closely related to the facility location problem and some scheduling problems and have been intensively studied in the literature. In this paper, extensions of the problem are defined and studied. The issue is to buy all the necessary products for a minimum total possible price. This includes all prices of products as well as shipping costs. Studies in this paper include the ISOP with price sensitive discounts and a newly defined optimization problem: the ISOP including two different discounting functions, namely a shipping cost function as well as a price discounting function. First, these are formulated as mathematical programming problems. Then, some algorithms are constructed and extensively tested in a computational experiment.     

Reducing cybersickness by implementing texture blur in the virtual reality content

Virtual Reality - Virtual reality facilities have matured in recent years; however, cybersickness has yet remained a major issue in the virtual reality content. The researchers in this study...     

Combined blur and affine moment invariants and their use in pattern recognition

The paper is devoted to the recognition of objects and patterns deformed by imaging geometry as well as by unknown blurring. We introduce a new class of features invariant simultaneously to blurring with a centrosymmetric PSF and to affine transformation. As we prove in the paper, they can be constructed by combining affine moment invariants and blur invariants derived earlier. Combined invariants allow to recognize objects in the degraded scene without any restoration.     

Statistical denoising of signals in the S-transform domain

In this paper, the denoising of stochastic noise in the S-transform (ST) and generalized S-transform (GST) domains is discussed. First, the mean power spectrum (MPS) of white noise is derived in the ST and GST domains. The results show that the MPS varies linearly with the frequency in the ST and GST domains (with a Gaussian window). Second, the local power spectrum (LPS) of red noise is studied by employing the Monte Carlo method in the two domains. The results suggest that the LPS of Gaussian red noise can be transformed into a chi-square distribution with two degrees of freedom. On the basis of the difference between the LPS distribution of signals and noise, a denoising method is presented through hypothesis testing. The effectiveness of the method is confirmed by testing synthetic seismic data and a chirp signal.     

Motion blur identification in noisy images using mathematical models and statistical measures

Motion blur is one of the most common blurs that degrades images. Restoration of such images is highly dependent on estimation of motion blur parameters. Since 1976, many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms are different in their performance, time complexity, precision and robustness in noisy environments. In this paper, we have presented a novel algorithm to estimate linear motion blur parameters such as direction and length. We used Radon transform to find direction and bispectrum modeling to find the length of motion. Our algorithm is based on the combination of spatial and frequency domain analysis. The great benefit of our algorithm is its robustness and precision in noisy images. We used statistical measures to prove goodness of our model. Our method was tested on 80 standard images that were degraded with different directions and motion lengths, with additive Gaussian noise. The error tolerance average of the estimated parameters was 0.9° in direction and 0.95 pixel in length and the standard deviations were 0.69 and 0.85, respectively.     

Moment forms invariant to rotation and blur in arbitrary number of dimensions

We present the construction of combined blur and rotation moment invariants in arbitrary number of dimensions. Moment invariants to convolution with an arbitrary centrosymmetric filter are derived first, and then their rotationally invariant forms are found by means of group representation theory to achieve the desired combined invariance. Several examples of the invariants are calculated explicitly to illustrate the proposed procedure. Their invariance, robustness, and capability of using in template matching and in image registration are demonstrated on 3D MRI data and 2D indoor images.     

FastICA在功耗分析去噪中的应用

提出了一种应用独立成分分析技术提高功耗分析中信号信噪比的方法。搭建了侧信道分析平台,采集了功耗数据,并对功耗数据进行了基于负熵的快速固定点算法的去噪实验,信噪比提升19 dB。最后提出了通过更换g函数实现优化的方式,对不同g函数在功耗分析中的去噪能力进行了对比。结果表明,更换后的去噪能力比之前提高4 dB以上。     

Legendre moment invariants to blur and affine transformation and their use in image recognition

The processing of the images simultaneously degraded by blur and affine transformation has become a key task in many applications and many novel methods are designed specifically for it in which the moment-based methods play an important role. However, the existing moment-based methods all resort to non-orthogonal moments invariants which have problem of information redundancy and are sensitive to noise. In this paper, we construct a new set of combined invariants of orthogonal Legendre moments which hold for blur and affine transformation together. The experimental results show that the proposed invariants have better discriminative power and robustness to noise with the comparison to other invariants.