Optimization of regularization parameters for electricalimpedance tomography

The image quality and computation speed are bounded up with regularization parameters. To improve the ill-posed property of the inverse problem of electrical impedance tomography (EIT), a novel approach, which is based on the product of the residual norm and the solution norm(PRS), is presented to optimize the Tikhonov regularization parameters of EIT. To verify the feasibility and effectiveness of the proposed method, five simulations of image reconstruction, together with a tank experiment, have been carried out with considering different sizes, locations, conductivity distributions and numbers of the target areas as well as the scenarios of the data with noises. The encouraging results demonstrate that the proposed optimization approach can identify the relatively optimal regularization parameter quickly and has better noise immunity, and it also enhances the quality of the reconstructed images significantly compared with the conventional L-curve method.     

Image Restoration Using Adopted Regularization Technology

Image restoration is aimed to recover the original scene from its degraded version. Based on the customary regularization technology and the property of the human visual systems, a new method for image restoration based on adopted regularization technology is presented. This technique is achieved by adopting the parameters of the residual term and the regularization term of each pixel in different part of the image. We can get the parameters of each pixel by computing the local variance at each point. Simulation results demonstrate that the method improves restored image quality and does not slow down the convergence of the evaluation function.     

Image edge detection algorithm in fuzzy domain

An image edge detection algorithm in fuzzy domain is proposed, which combines adaptive fuzzy enhancement and multi direction fuzzy morphology to detect the edges of fuzzy image. The adaptive fuzzy enhancement method enhances the fuzzy image within blocks with sliding windows to avoid losses of the real edges resulting from enhancing with single threshold for the whole image and lead strong adaptive ability to image region variance. The multi direction fuzzy mathematical morphology operates on the enhanced fuzzy image with structure elements of multiple different directions to extract the real edges with directionality and restrain non directional noise. Experiments show the algorithm can detect fuzzy image edge effectively with strong antinoise ability.     

Novel Image-adaptive Watermarking Algorithm

Digital watermarking is an effective method to provide copyright protection for digital media. A blind image- adaptive watermarking algorithm based on DCT domain is presented. The algorithm selects adaptively the step size of quantization to embed the watermark bits by modifying DC components via the human visual system model and local characteristics of image; at the same time some AC components of the blocks with less perceptual capacity are also modified to embed the same watermark bits by using quantization. Thus the relation between robustness and imperceptibility can be well balanced. The algorithm recovers the watermark without any reference to the original image. Compared with the similar algorithm reported in the literature, this method can give better image quality and is better robust against noise and commonly used image processing techniques.     

Image inpainting based on nonlinear anisotropic diffusions

Following the features of curvature and gradient at image edges,an image inpainting method based on nonlinear anisotropic diffusion is proposed.The diffusion can be conducted with different directions and different intensities according to the geometric features of the inpainted images.An adaptive factor is introduced based on the curvature and gradient of the image local geometric information,which can control the diffusion direction and diffusion intensity.At the edges in images,the diffusion coefficients are large for the horizontal directions,while the diffusion coefficients are small for the vertical directions.At the smooth regions,the diffusion coefficients are the same for different directions and they are usually large.Compared with the typical total variation method,the curvature derivation diffusion method,and P-laplace constant variation method,the experimental results show that the proposed method can improve the qualities of the inpainted images     

Single sample face recognition based on B-spline and image gradient

A single sample face recognition algorithm based on B-spline and image gradient is proposed. Image gradient method for face recognition has advantage of illumination invariant. But the recognition rate will be greatly decreased when the image contains noise which will seriously influence gradient information. Traditional methods to reduce noise smooth image at the same time and image gradient reorganization rate will be reduced. As the B-spline filter has the feature which can adjust the order, B-spline filter with different orders can be selected according to the image noise value to minimize noise while preserve image gradient information. Experiments prove that using B-spline and image gradient algorithm can achieve a better recognition rate than traditional filtering method on single sample face recognition problem.     

Corner detection based on evolution difference of plannar curves in B spline scale space

In order to improve image corner detection precision and efficiency, a novel corner detection algorithm based on B spline scale space evolution difference is proposed. The norm of DoB is defined as corner response function to evaluate the multi scale evolution difference. The DoB corner detector confluents the image boundary features with different scales, which can not only strengthen the response of the feature points, but also depress the influence of noise. Among all corner detection algorithms based on B Spline, the proposed algorithm is relatively lower for computation complexity and faster. The comparative experiments demonstrate that the proposed algorithm works well for localization, robustness against noise, as well as invariance to rotation and scalability.     

An edge detection method based on a good point set genetic algorithm

In order to improve the convergence rate of genetic algorithms based on edge detection, a novel edge detection method based on a good point set genetic algorithm (GGA) was proposed. The proposed method designed the crossover operation with the theory of good point set in which the progeny inherits the common genes of the parents which represent its family so as to improve the convergence rate of the genetic algorithm. Furthermore, before the algorithm was used for edge detection, the feature space of the image grey level was transformed into the feature space of the fuzzy entropy. Dissimilarity enhancement processing next was applied to the image by using a fuzzy entropy theory to filter the non edge pixels so as to reduce the scale of the solution domain. This approach offered another efficient way to improve the convergence rate. Experimental results show the proposed algorithm performs very well in terms of convergence rate. The detected edge image is well localized, thin, and robustly resistant to noise.     

Segmentation of Color Image Based on Feature Divergence and Fuzzy Theory

In order to improve general adaptive capability of algorithm,the new color image segmentation algorithm based on feature divergence and fuzzy theory(FDCIS) is proposed.The algorithm introduces feature divergence and fuzzy dissimilarity function into calculation in order to measure the dissimilarity of feature vector,clusters data by means of feature divergence,and accomplishes the merge of image region.The experimental results demonstrate that the color image segmentation result of the proposed approach reduce calculation on large sample of color image,simply and effectively solve over-segmentation of color image,avoid the dependence of the algorithm on initial condition,and hold favorable consistency in terms of human perception.     

Multifocus image fusion scheme based on feature contrast in lifting stationary wavelet domain

A novel multifocus image fusion method based on lifting stationary wavelet transform (LSWT) is proposed. The selection principles, namely fusion rules of different subband coefficients, are discussed in detail. Local feature contrast is presented according to the human vision system (HVS), which is highly sensitive to the local image contrast level. Then, the fusion rule for the low-frequency subband coefficients fusion is introduced. To choose the high frequency subband coefficients, another local feature contrast is developed according to the human vision which is often sensitive to edges and directional features, but insensitive to real luminance at independent positions. Then, a novel fusion rule is proposed for fusion of the high frequency subband coefficients. Experimental results demonstrate that the proposed image fusion method is effective and can provide better performance in fusing multifocus images than the traditional contrast-based image fusion algorithms in term of informal visual inspection and objective criteria in multi-focus image fusion.     

OPTIMIZATION OF BIPARTITE JOINS WITH GRAPH THGORY

An optimization approach for multi-relational joins based on the matching concept in graph theory is introduced. The basic idea lies in constructing a join graph from an expression of N-relation query, and seeking for a maximun matching with minimun total weight. The algorithm for bipartition(X, Y) is proposed in this paper.     

Open-electrical impedance tomography with variation regularization algorithm

Open electrical impedance tomography (OEIT) with fixed electrode row is proposed to overcome the clinical application problems in closed electrical impedance tomography such as poor model adaptability, electrode position error, and inflexibility. Variation regularization algorithm (VRA) using variations function as regularization penalty term is proposed to save the more serious ill-posed inverse problem of OEIT. Simulation and experiment results show that the inverse problem of OEIT can be efficiently solved by VRA. The position, size and the relative value of conductivity of target at shallower position below electrodes can be clearly reflected by the restored image. OEIT is more potentially practical and effective in clinical applications.     

Effective Coefficient Threshold for Complex Wavelet Transform

This paper constructs complex wavelet which used for suppressing white noise in PD,and then analyses some disadvantages of several existing wavelet threshold and presents a method of Effective Wavelet Coefficient(EWC) according to the characteristic of modulus maximum of PD signals.Then it is compared with maximin theory threshold selection and Stein unbiased risk estimate theory threshold selection.The findings suggest that the method of EWC is adaptive,and it can suppress noise completely with small distortion of PD signal.     

Digital modulation mode recognition based on multi-classclassification of support vector machine

To solve the overfitting, underfitting and local minimum existing in neural networks, a digital modulation mode recognition method based on support vector machine (SVM) is proposed. Seven characteristic parameters are extracted from instantaneous amplitude, instantaneous phase, instantaneous frequency, frequency spectrum, and changes in characteristics of the envelope to train support vector machine. Compared with the existing algorithms, using binary tree theory to design multi-class classifier has the features of simple, high-speed, high-precision. The simulation results indicate that the scheme can achieve 97% recognition accuracy when the signal to noise ratio (SNR) is above 15 dB with the AWGN channel.     

频域滤波的水泥路面图像降噪增强方法

针对在水泥路面裂缝图像识别中刻槽和纵向条带状形成的噪声干扰,提出了一种基于频域滤波的降噪增强方法。首先,分析了这两类噪声的频谱特征,并根据空域图像中刻槽的周期性,推导了频域图像中刻槽谱峰的位置,设计了消除这些谱峰的系列滤波器。根据纵向条带状干扰属于低频成分的特性,设计了抑制低频、增强高频的滤波器,经优化滤波器参数达到消除这类干扰的效果。实验结果表明,该方法能够消除两类噪声干扰,同时,增强了裂缝的对比度,为水泥路面裂缝识别提供了理论基础。     

FAST ADAPTIVE FILTERING ( FAF ) FOR THE PROCESS DETECTION CONTROL SYSTEMS

The limitation of LMS adaptive noise cancelling (ANC ) is analyzed here. Based on the discussing of general signal and noise model of process detection control system, a fast adaptive filtering(FAF) is proposed on the basis of modelling the correlativity between the noise and it's correlative noise with the view of large noise. Fast transversal filtering ( FTF ) adaptive, algorithms is used to imitate the correlative modelling. Both simulation and experiments show that the method presented in this paper is suitable for the process detection control system , and it exhibts better results than the LMS noise cancelling method.     

A blind adaptive reduced-rank receiver for ultra-wideband communication systems

A low-complexity blind adaptive receiver for ultra-wideband (UWB) systems in the presence of both multiple access interference (MAI) and inter symbol interference (ISI) is proposed, which is composed of two stages. In the first stage, a reduced-rank algorithm based on the multi-stage Wiener filter (MSWF) is considered to suppress the MAI and extract multi-path components. Channel estimation using the projection approximation subspace tracking with deflation (PASTd) algorithm and multi-path combining are then performed to further enhance the signal to interference plus noise ratio (SINR) of the desired user in the second stage. The simulation results show that the proposed receiver exhibits reasonably good bit error ratio (BER) performance compared with those of matched filter, conventional Rake receiver, decorrelating Rake receiver and adaptive receiver based on constant modulus algorithm (CMA). Moreover, it has faster convergence speed and less complexity.     

Digit Weight and Development of a Virtual Noise Analyzer

Traditional noise octave analyzers consist of filter network and weight network based on hardware. In order to realize a noise octave analysis by software, a digit weight method based on FFT is proposed. Using the method, digit weight and digit system error modification for virtual noise octave analyzer can be realized. Because of digit weight and digit system error modification, precision and stability of the instrument can be improved greatly. As an application, which combines the method of digit weight with the technique of digit system error modification, a low cost and high precision virtual real time noise octave analyzer is developed.     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

Strategy and implementation of resolve overlapping spectra based on curve fitting

Aming at the overlapping peaks in spectrum analysis, a novel method of curve fitting based on Gaussian function is presented to resolve the overlapping peaks. The theory of curve fitting is introduced firstly, and then an algorithm is proposed based on both the minimum separable peak peak interval and the curve fitting error. In the peak positioning, a gradually strict strategy is introduced to exclude the fake peaks. The resolution of several kinds of overlapping peaks with computer simulated noise has been performed and discussed in details. The calculated results indicate that the peak positions can be extracted effectively, even in the case of serious overlapped, and clearly show the effectiveness of the proposed method.