Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation L1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i) each channel in a color image is contaminated independently, and contaminative components are independent and identically distributed; (ii) in a natural image the gradients of different components of a pixel are similar to one another. This method divides components into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the components are divided into the corrupted and the noise-free components; if the image is corrupted by random-valued impulses, the components are divided into the corrupted, noise-free, and the possibly corrupted components. Components falling into different categories are processed differently. If a component is corrupted, modified total variation diffusion is applied; if it is possibly corrupted, scaled total variation diffusion is applied; otherwise, the component is left unchanged. Simulation results demonstrate its effectiveness

Lightweight Proofs of Retrievability for Electronic Evidence in Cloud

Proofs of Retrievability (PoR) is one of the basic functions of electronic evidence preservation center in cloud. This paper proposes two PoR schemes to execute the workflow of evidence preservation center, which are named Finer Grained Proofs of Retrievability (FG-PoR) and More Lightweight Proofs of Retrievability (ML-PoR). The two PoR schemes do not use multi-replication technology or erasure code technology, but employ the verification tags and signatures to implement provable data possession and data recovery dual functions. When some data blocks have been lost in Archive Storage Area (ASA), FG-PoR can recover each data block of evidence matrix, but ML-PoR can only recover a column of evidence matrix. The analysis results show our two PoR schemes do not only provide the integrity verification guarantee but also have robust recovery guarantee to electronic evidence in cloud. The two schemes can allow for lower computation and storage costs than other similar schemes; moreover, ML-PoR can provide lower costs than FG-PoR

Chaos-Based Image Encryption Algorithm Using Decomposition

The proposed chaos-based image encryption algorithm consists of four stages: decomposition, shuffle, diffusion and combination. Decomposition is that an original image is decomposed to components according to some rule. The purpose of the shuffle is to mask original organization of the pixels of the image, and the diffusion is to change their values. Combination is not necessary in the sender. To improve the efficiency, the parallel architecture is taken to process the shuffle and diffusion. To enhance the security of the algorithm, firstly, a permutation of the labels is designed. Secondly, two Logistic maps are used in diffusion stage to encrypt the components. One map encrypts the odd rows of the component and another map encrypts the even rows. Experiment results and security analysis demonstrate that the encryption algorithm not only is robust and flexible, but also can withstand common attacks such as statistical attacks and differential attacks. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.301

A Double-Efficient Integrity Verification Scheme to Cloud Storage Data

This paper proposed two integrity verification schemes based on Schnorr Signature Scheme. One is safety integrity verification scheme (SIVS). Another is efficient integrity verification scheme (EIVS). They are difference in characteristics. EIVS has good computational costs while SIVS has high security guarantee. However, they are similar in work. The cloud storage server will choose a set of file blocks and verification blocks randomly while the user sends a challenge, and generate response values to send to the user. The user generates a set of signatures to verify the values. The aim is to check whether the cloud storage server preserves perfectly the user's file or not. In contrast with other schemes, the approach not only has double integrity verification guarantee schemes but also pay lower costs for communication and computation

Optimizing the Procurement of IaaS Reservation Contracts via Workload Predicting and Integer Programming

Cloud-based web applications are proliferating fast. Owing to the elastic capacity and diverse pricing schemes, cloud Infrastructure-as-a-Service (IaaS) offers great opportunity for web application providers to optimize resource cost. However, such optimization activities are confronting the challenges posed by the uncertainty of future demand and the increasing reservation contracts. This work investigates the problem of how to minimize IaaS rental cost associated with hosting web applications, while meeting the demand in the future business cycle. First, an integer liner program model is developed to optimize reservation-contract procurement, in which reserved and on-demand resources are planned for multiple provisioning stages as well as a long-term plan, e.g., twelve stages in an annual plan. Then, a Long Short-Term Memory (LSTM) based algorithm is designed to predict the workload in the future business cycle. In addition, the approaches for determining virtual instance capacity and the baseline workload of planning time slot are also presented. Finally, the experimental prediction results show the LSTM-based algorithm gains an advantage over several popular models, such as the Holter–Winters, the Seasonal Autoregressive Integrated Moving Average (SARIMA), and the Support Vector Regression (SVR). The simulations of resource planning show that the provisioning scheme based on our reservation-optimization model obtains significant cost savings than other typical provisioning schemes, while satisfying the demands

Rectification of License Plate Images Based on HT and Projection

It is crucial to segment characters correctly and improve rate of correct character recognition when processing automobile license plates corrections. In this paper, two algorithms are proposed to obtain the horizontal tilt and vertical shear angles. The transformation matrix for images rectification is given and the subpixel issue is solved. Some experiments were done to test the algorithms. Experimental results show that the algorithm is robust, flexible and effective.DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.309

A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal

Impulsive noise removal usually employs median filtering, switching median filtering, the total variation L1 method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images