图像Inpainting技术原理及在包装印刷图像处理中的应用

在包装印刷行业图像修复问题需要有经验的技术人员进行复杂的手工处理,随着计算机图像处理领域对图像自动处理技术的讨论,INPAINTING技术对包装印刷图像处理提供了新的方法和方向.主要介绍了图像自动修复技术的原理、发展,以及在包装印刷行业的应用.     

Improved security in the genetic algorithm-based image steganography scheme using Hilbert space-filling curve

This paper presents the security analysis of a recently proposed data hiding scheme by Kanan and Nazeri [A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Syst Appl. 2014;41(14):6123–6130]. Security of the scheme depends upon a secret key\chromosome constructed using a genetic algorithm. The key specifies offsets and patterns for data embedding. By performing the key-sensitivity analysis and exploiting image pixel correlation, it is shown in the present paper that the secret data can be easily revealed. Simulation results demonstrate that just 11 bits out of the 27-bit secret key are sufficient to reveal the data. To overcome the problem, an improvement is suggested in the data embedding pattern that demonstrates high data security. Bitwise analysis of the secret key used in the modified approach exhibits high sensitivity to change in the key.     

Optimal reduction of noise in image processing using collaborative inpainting filtering with Pillar K-Mean clustering

Digital image processing is a mechanism for analysing and modifying the image in order to improve the quality and also to manage the unwanted involvement of noises. In image processing, noise is characterized as an unwanted disturbance which occurs while capturing the actual image thus affecting the quality of the image. Hence, noise formation is considered as a perilous issue and the reduction of noise is considered as an awkward process. Nowadays, almost in all fields of science and technology, digital image processing is increasing rapidly, so there arises the need for de-noising to cure the noised image. The main objective of this paper is to overcome the issue of noise and also to increase the quality and pixel value of the image. An advanced methodology known as collaborative filtering and Pillar K-Mean clustering is discussed in this paper to overcome the abovementioned problem. Initially, distinct pure images are taken as the dataset and three types of noises are added to the corresponding image to make it as a noised one. Hence, the unspecified noise is resolved on the basis of a hybrid combination of algorithms of collaborative filtering with the image inpainting method. Sequentially, the low-density noises, such as random noise and poison noise, are recovered by the implementation of collaborative filtering, and the high-density salt and pepper noise are recovered by the image inpainting method. Based on the GLCM (Grey Level Co-occurrence Matrix) feature, the normal image and the noised image are used for the clustering process. Then the de-noised image is evaluated to find the efficiency on the basis of few parameters such as SNR (Signal to Noise Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SSI (Structural Similarity Index). Accordingly, the evaluated images are further withstood for clustering to differentiate the noises by applying the proposed clustering methodology. Then the evaluated images are verified on the basis of a few parameters such as Silhouette Width, Davies–Bouldin Index and Dunn Index. The proposed methodology is run on the platform of Mat Lab. Finally, the proposed methodology is considered as an efficient method for settling the issue in digital image de-noising.     

一种新的基于可逆矩阵的具有完整性检验能力的图像加密方案

为了保护图像信息的安全,论文利用组合理论知识,提出一种新的基于可逆整数矩阵的、具有完整性检验能力的图像加密方案。应用该方案将一个灰度图像加密生成一个脆弱的噪声密图,解密过程是加密的简单逆过程。密图的完整性可以凭借人类视觉系统进行检验,不需要任何复杂的计算。当密图遭受恶意篡改时,解密得到一个噪声图,无法得到原始图像的任何信息。论文加密方案的密钥简单,且密钥空间足够大。理论分析和实验结果证明了,该方案为图像加密提供了一种高安全性和有效的机制。     

A reversible data-hiding method for palette-based images with capacity optimization

This study presents a reversible data-hiding scheme based on palette image histogram characteristics, in which the least frequent color entries and the most frequent color entries are identified to imbed data. Data are concealed by sacrificing many sets of the least frequent colors, and placing one of the most frequent colors in their positions in the palette to make a ‘cluster’ for each set. Different bits of data can be imbedded using different sized clusters. A capacity optimization scheme is then developed to obtain the maximum capacity by estimating all possible combinations of clusters. For image reconstruction, making the host image reversible requires saving the overhead information caused by sacrificing the least frequent colors. Effectiveness of the proposed method is also demonstrated on test images by showing the capacity and distortion. Importantly, the proposed method has a high imbedding capacity and excellent marked image quality.     

Secret image transmission based on VQ and data embedding

A new secret image transmission scheme suitable for narrow communication channel is proposed in this article. A set of secret images can be simultaneously and efficiently delivered to the receiver via a small and meaningless data stream by the proposed scheme. To reduce the volume of secret images, a codebook is first generated and these secret images are encoded into binary indexes based on the vector quantization (VQ) technique. The compressed message is then embedded into the VQ codebook utilized in the encoding procedure by an adaptive least‐significant‐bits (LSB) modification technique. For the purpose of security, the slightly modified codebook is further encrypted into a meaningless data stream by the AES cryptosystem. Simulation results show that the proposed scheme provides an impressive improvement both in the visual quality of the extracted secret images at the receiver and in the hiding capacity of the cover medium. Experimental data also reveal the feasibility of the proposed secret image transmission scheme for limited‐bandwidth environment. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 1–9, 2007     

A novel scheme for image sharpness using inflection points

It is useful to increase the sharpness in medical images. This improvement can help medical diagnoses and treatment outcomes for patients. Noise overshoot and oversharpening effects are common artifacts of conventional sharpening algorithms. In this work, we propose an adaptive sharpness algorithm that achieves better sharpening effects than standard methods. As the pixel value changes abruptly at an edge, a method that adequately emphasized sudden variations in pixel values is effective for use in the sharpening process. Measurement of the gradient norm value of each pixel is calculated and compared to a threshold value to produce a curve. The curve quickly drops at the initial stage and then decreased more slowly for higher norm values. To distinguish the edge, an inflection point is determined by taking the second derivative of the curve and identifying the turnover point (where the curvature changes sign). Norm values higher than the inflection point are identified as belonging to the edge, and a simple sharpness filter is adaptively applied to these points. The proposed approach yielded better results than global filtering and a conventional unsharp masking approach when evaluated using Pratt's figure of merit.     

Robust medical image registration based on phase congruency and regional mutual information

Abstract

In this paper, a new approach of multi-modality image registration is represented with not only image intensity, but also features describing image structure. There are two novelties in the proposed method. First, instead of standard mutual information based on joint intensity histogram, a graph-based implementation of multi-dimensional regional mutual information is employed, which allows neighbourhood information to be taken into account. Second, a new feature image is obtained by means of phase congruency, which is invariant to brightness or contrast changes. By incorporating these features and intensity into regional mutual information, we can combine aspects of both structural and neighbourhood information together, which offers a more robust and a high level of registration accuracy that is essential in application to the medical domain.     

An adaptive scheme for compressed video steganography using temporal and spatial features of the video signal

Steganography is the art and science of data hiding. This article proposes an adaptive steganographic algorithm that embeds secret data in a compressed video stream using temporal and spatial features of the video signal and human visual system characteristics. Qualified‐DCT coefficients of I‐VOP and motion vectors of P‐VOP and B‐VOP are used for spatial and temporal features of the video, respectively. Embedded‐data are extracted without using the original video and there is no need for full decompression. Experimental results demonstrate that the proposed algorithm has high imperceptibility and capacity. Furthermore, the bit rate remains approximately constant. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 306–315, 2009     

Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform

Many types of medical images must be fused, as single‐modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low‐ and high‐frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low‐ and high‐frequency components. The membership degree of low‐frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High‐frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition.     

An efficient and secure partial image encryption for wireless multimedia sensor networks using discrete wavelet transform,chaotic maps and substitution box

Wireless Sensor Networks (WSN) is widely deployed in monitoring of some physical activity and/or environmental conditions. Data gathered from WSN is transmitted via network to a central location for further processing. Numerous applications of WSN can be found in smart homes, intelligent buildings, health care, energy efficient smart grids and industrial control systems. In recent years, computer scientists has focused towards findings more applications of WSN in multimedia technologies, i.e. audio, video and digital images. Due to bulky nature of multimedia data, WSN process a large volume of multimedia data which significantly increases computational complexity and hence reduces battery time. With respect to battery life constraints, image compression in addition with secure transmission over a wide ranged sensor network is an emerging and challenging task in Wireless Multimedia Sensor Networks. Due to the open nature of the Internet, transmission of data must be secure through a process known as encryption. As a result, there is an intensive demand for such schemes that is energy efficient as well as highly secure since decades. In this paper, discrete wavelet-based partial image encryption scheme using hashing algorithm, chaotic maps and Hussain’s S-Box is reported. The plaintext image is compressed via discrete wavelet transform and then the image is shuffled column-wise and row wise-wise via Piece-wise Linear Chaotic Map (PWLCM) and Nonlinear Chaotic Algorithm, respectively. To get higher security, initial conditions for PWLCM are made dependent on hash function. The permuted image is bitwise XORed with random matrix generated from Intertwining Logistic map. To enhance the security further, final ciphertext is obtained after substituting all elements with Hussain’s substitution box. Experimental and statistical results confirm the strength of the anticipated scheme.     

Optimisation of the multi-depots pick-up and delivery problems with time windows and multi-vehicles using PSO algorithm

Many sectors in the transport industry are concerned about the vehicle routing problem (VRP), hence the growing interest of researchers for this type of problem and its variants. This is due essentially to its many real applications in logistics for the transport of goods. The originality and contribution of our work is that we have dealt a problem that combines several variants: multiple vehicles (m), multiple depots (MD), pickup and delivery problem (PDP) with time windows (TW). Hence the notation of our problem: m-MDPDPTW. In this paper, we present the m-MDPDPTW, which is an optimisation problem belonging to the category of NP Hard problems. This problem must meet requests for transport between customers and suppliers satisfying precedence, capacity and time constraints. The goal is to find the best solution, which is the best route minimising the total travelled distance. To solve and optimise our m-MDPDPTW, we have developed a new algorithm based on the particle swarm optimisation (PSO) method. The performance of this new approach is tested on data set instances of Li and Lim's benchmark problems in which we have added multiple depot locations. Comparing with prior works, our proposed approach gave better results by decreasing the distance for several studied instances.     

A novel framework for spatiotemporal monitoring and post-signal diagnosis of processes with image data

Advances in digital equipment and organizations' interest in having comprehensive and real-time information about products have increased the use of machine vision systems in organizations. In this paper, to monitor a sensory quality characteristic of a product based on images, the residual matrix of the intensity values of the nominal and captured images is divided into specific regions; then, the equality of the means of the regions is tested based on one-way ANOVA. To do so, a P-value–based control chart is applied to detect the out-of-control state as soon as possible. If an out-of-control alarm is received, Dunnett's test is used to identify region(s) with significant differences in the means of residuals (defective location[s] in the image) compared with other regions. After the locations of defective regions are identified, the change point of the process is estimated by using the maximum likelihood estimation approach. The performance of the proposed procedure is compared with some of the previous approaches in the literature. Then, the proposed procedure is implemented in a real-world case. The simulation study demonstrates the merits of the proposed procedure: It is not limited to specific geometric types of faults; it has the ability to detect multiple faults in the image; it takes less computational time; and it performs well in estimating the real time of change, as well as the location(s) and dimension(s) of the fault(s).     

A robust optimization approach for integrated steel production and batch delivery scheduling with uncertain rolling times and deterioration effect

Efficient collaboration between various sub-processes of steel production is of considerable significance, which directly affects a product’s production cycle and energy consumption. However, current collaborative optimisation models and methods in steel production are still limited: (1) Most of the current collaborative manufacturing problems in steel production focus on obtaining joint schedule between steel-making and continuous casting (SCC), and the works considering continuous casting and hot rolling (CCHR) are very few. (2) The processing time is assumed as a constant in most of the existing SCC scheduling models. However, the rolling time of a product in hot rolling operation is actually uncertain and deteriorating. (3) Exact algorithms cannot be applied to solve the complicated collaborative optimisation problems because of their high complexities. To address these problems, we propose an integrated CCHR and batch delivery scheduling model where interval rolling time and linear deterioration effect are considered. With the concept of min–max regret value, we formulate the collaborative optimisation problem as a robust optimisation problem. Instead of using the exact algorithm, we develop an Improved Variable Neighborhood Search (IVNS) algorithm incorporated a novel population update mechanism and neighbourhood structures to solve the robust optimisation problem. Moreover, we develop an exact algorithm that combines CPLEX solver and two dynamic programming algorithms to obtain the maximum regret value of a given rolling sequence. The results of computational experiments show the excellent performance of the proposed algorithms.

Abbreviations: IVNS: improved variable neighbourhood search; TOPSIS: technique for order of preference by similarity to ideal solution; PUM-TOPSIS: population update mechanism based on TOPSIS; DP: dynamic programming; NSs-PUC: neighbourhood structures based on the parameterised uniform crossover; SNRT: shortest normal rolling time; SNRT-DP: DP algorithm based on SNRT rule; BRKGA: biased random-key genetic algorithm; SCC: steelmaking and continuous casting; MINP: mixed integer nonlinear programme; CCHR: continuous casting and hot rolling; PSO: particle swarm optimisation; GA: genetic algorithm; VNS-HS: variable neighbourhood search and harmony search; HPSO?+?GA: hybrid PSO and GA; SA: simulated annealing; B&B: branch-and-bound; TPSO: two-phase soft optimisation; TSAUN: tabued simulated annealing with united-scenario neighbourhood; VNS: variable neighbourhood search; ABC: artificial bee colony; PRVNS: population-based reduced variable neighbourhood search; NS1: neighbourhood structure 1; NS2: neighbourhood structure 2; DE: differential evolution; WSR: Wilcoxon signed-rank test; ENS: exchange neighbourhood structure; IVNS-ENS: IVNS with ENS; RPI: relative percentage increase; ARPI: average RPI; SD: standard deviation.     

A genetic algorithm for dynamic inbound ordering and outbound dispatching problem with delivery time windows

This article considers an inbound ordering and outbound dispatching problem for a single product in a third-party warehouse, where the demands are dynamic over a discrete and finite time horizon, and moreover, each demand has a time window in which it must be satisfied. Replenishing orders are shipped in containers and the freight cost is proportional to the number of containers used. The problem is classified into two cases, i.e. non-split demand case and split demand case, and a mathematical model for each case is presented. An in-depth analysis of the models shows that they are very complicated and difficult to find optimal solutions as the problem size becomes large. Therefore, genetic algorithm (GA) based heuristic approaches are designed to solve the problems in a reasonable time. To validate and evaluate the algorithms, finally, some computational experiments are conducted.     

A pH-dependent colon-targeted oral drug delivery system using methacrylic acid copolymers. II. Manipulation of drug release using Eudragit L100 and Eudragit S100 combinations

Tablets containing mesalazine as a model drug were coated using various combinations of two methacrylic acid copolymers, (Eudragit® L100 and Eudragit S100) by spraying from aqueous systems. The Eudragit L100-Eudragit S100 (w/w) combinations studied were 1:0, 4:1, 3:2, 1:1, 2:3, 1:4, 1:5, and 0:1. The coated tablets were tested in vitro for their suitability for pH-dependent colon-targeted oral drug delivery. The dissolution profiles of the drug obtained from the studied tablets demonstrate that the release of the drug could be manipulated by changing the Eudragit L100-Eudragit S100 ratios in the combinations within the pH range between 6.0 and 7.0 in which the individual polymers are soluble, and a coating formulation consisting of a combination of the two polymers can overcome the issue of high gastrointestinal (GI) pH variability among individuals. The results also demonstrate the feasibility of using aqueous dispersions of Eudragit L100-Eudragit S100 combinations for coating tablets for colon-targeted delivery of drugs, and that the formulation can be adjusted to deliver drug(s) at any other desirable site of the intestinal region of the GI tract in which pH of the fluid is within the range 6.0 to 7.0. For colon-targeted delivery of drugs, the proposed combination system is superior to tablets coated with either Eudragit L100 or Eudragit S100 alone.     

一种利用EAM实现高效下变频和远程传送光LO信号的ROF结构

提出了一种新颖的光纤无线通信(ROF)系统中高效全光下变频的方案.用此方案可远程传送光本振信号,在无需改变中心站配置和复杂度的情况下,仅需在基站使用一个电吸收调制器就能同时实现对上行链路数据的下变频并进行调制,使得基站不需要光源和本振射频源,大大减小了基站的复杂度,节约了系统成本.通过模拟和实验表明,光本振信号在光纤中传输20km后,对上行链路数据进行相干接收,其功率代价小于4dB.     

A new approach to medical image stitching using minimum average correlation energy filter and peak to side‐lobe ratio

Standard X‐ray images using conventional screen‐film technique have a limited field of view and failed to visualize the entire long bone on a single image. To produce images with whole body parts, digitized images from the films that contain portions of the body parts are assembled using image stitching. This article presents a new medical image stitching method that uses minimum average correlation energy filters to identify and merge pairs of X‐ray medical images. The effectiveness of the proposed method is demonstrated in the experiments involving two databases that contain a total of 40 pairs of overlapping and nonoverlapping images. Then the experimental results are compared to those of the normalized cross correlation (NCC) method. It is found that the proposed method outperforms the NCC method in identifying both the overlapping and nonoverlapping medical images. The efficacy of the proposed method is further vindicated by its average execution time which is about five times shorter than that of the NCC method. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 166–171, 2012     

A novel approach in multimodality medical image fusion using optimal shearlet and deep learning

Multi-modality medical image fusion (MMIF) procedures have been generally utilized in different clinical applications. MMIF can furnish an image with anatomical as well as physiological data for specialists that could advance the diagnostic procedures. Various models were proposed earlier related to MMIF though there is a need still exists to enhance the efficiency of the previous techniques. In this research, the authors proposed a novel fusion model based on optimal thresholding with deep learning concepts. An enhanced monarch butterfly optimization (EMBO) is utilized to decide the optimal threshold of fusion rules in shearlet transform. Then, low and high-frequency sub-bands were fused on the basis of feature maps and were given by the extraction part of the deep learning method. Here, restricted Boltzmann machine (RBM) was utilized to conduct the MMIF procedure. A benchmark dataset was utilized for training and testing purposes. The investigations were conducted utilizing a set of generally-utilized pre-enrolled CT and MR images that are publicly accessible. From the usage of fused low and high level frequency groups, the fused image can be attained. The simulation performance results were attained and the proposed model was proved to offer effective performance in terms of SD, edge quality (EQ), mutual information (MI), fusion factor (FF), entropy, correlation factor (CF), and spatial frequency (SF) with respective values being 97.78, 0.96, 5.71, 6.53, 7.43, 0.97, and 25.78 over the compared methods.     

A bi-objective parallel machine problem with eligibility,release dates and delivery times of the jobs

The scheduling of parallel machines is a well-known problem in many companies. Nevertheless, not always all the jobs can be manufactured in any machine and the eligibility appears. Based on a real-life problem, we present a model which has m parallel machines with different level of quality from the highest level for the first machine till the lowest level for the last machine. The set of jobs to be scheduled on these m parallel machines are also distributed among these m levels: one job from a level can be manufactured in a machine of the same or higher level but a penalty, depending on the level, appears when a job is manufactured in a machine different from the highest level i.e. different from the first machine. Besides, there are release dates and delivery times associated to each job. The tackled problem is bi-objective with the criteria: minimisation of the final date – i.e. the maximum for all the jobs of their completion time plus the delivery time – and the minimisation of the total penalty generated by the jobs. In a first step, we analyse the sub-problem of minimisation of the final date on a single machine for jobs with release dates and delivery times. Four heuristics and an improvement algorithm are proposed and compared on didactic examples and on a large set of instances. In a second step an algorithm is proposed to approximate the set of efficient solutions and the Pareto front of the bi-objective problem. This algorithm contains two phases: the first is a depth search phase and the second is a backtracking phase. The procedure is illustrated in detail on an instance with 20 jobs and 3 machines. Then extensive numerical experiments are realised on two different sets of instances, with 20, 30 and 50 jobs, 3 or 4 machines and various values of penalties. Except for the case of 50 jobs, the results are compared with the exact Pareto front.