A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was achieved. Security criteria were computed after running a proposed encryption process. Then an adaptive fuzzy system decided whether to repeat the encryption process, terminate it, or run the next stage based on the achieved results and user demand. The SHA-512 hash function was employed to increase key sensitivity. Security analysis was conducted to evaluate the security of the proposed scheme, which showed it had high security and all the criteria necessary for a good and efficient encryption algorithm were met. Simulation results and the comparison of similar works showed the proposed encryptor had a pseudo-noise output and was strongly dependent upon the changing key and plain image.

Nowadays, Canny edge detector is considered to be one of the best edge detection approaches for the images with step form. Various overgeneralized versions of these edge detectors have been offered up to now, e.g. Saryazdi edge detector. This paper proposes a new discrete version of edge detection which is obtained from Shen-Castan and Saryazdi filters by using bilinear transformation. Different experimentations are conducted to decide the suitable parameters of the proposed edge detector and to examine its validity. To evaluate the strength of the proposed model, the results are compared to Canny, Sobel, Prewitt, LOG and Saryazdi methods. Finally, by calculation of mean square error (MSE) and peak signal-to-noise ratio (PSNR), the value of PSNR is always equal to or greater than the PSNR value of suggested methods. Moreover, by calculation of Baddeley's error metric (BEM) on ten test images from the Berkeley Segmentation DataSet (BSDS), we show that the proposed method outperforms the other methods. Therefore, visual and quantitative comparison shows the efficiency and strength of proposed method.

In this study, we review the elliptic differential operators and filters, and due to a wide range of elliptic operators, focus on a batch of elliptic operators with constant coefficients second order and then generalizing them to a higher order. Finally by discretization of the elliptic operators, we express and prove two theorems and show that the obtained filters are the high pass.

Denoising of natural images is a basic problem in image processing. The present paper proposes a new algorithm for image denoising based on the maximum a-posteriori (MAP) estimator in undecimated dual-tree complex wavelet transform. The undecimated dual-tree complex wavelet transform (UDT-CWT), along with the directional selectivity of the dual-tree complex wavelet transform (DT-CWT), offers exact translational invariance property through removing the down-sampling of filter outputs together with the up-sampling of the complex filter pairs of DT-CWT. These properties are very important in image denoising. The performance of the MAP estimator depends strongly on the probability of noise-free wavelet coefficients. In our proposed denoising method, multivariate t -distribution is applied as the prior probability of noise-free coefficients. The t -distribution can accurately model the statistics of wavelet coefficients, which have peaky and heavy-tailed characteristics. On the other hand, the multivariate model makes it possible to take into account the dependencies of wavelet coefficients and their neighbors. Also, in our work, the necessary parameters of the multivariate distribution will be estimated in a locally-adaptive way to improve the denoising results via using the correlations among the amplitudes of neighbor coefficients. Simulation results delineate that the proposed algorithm outperforms state-of-the-art denoising algorithms in the literature.

Reversible watermarking is a technique permitting lossless data hiding. In such a method, the lossless recovering of both watermark and host image is essential. For some applications, such as medical imaging and military systems, it is so vital not only to recover the host image exactly but also to increase security. To obtain these goals, a new reversible watermarking scheme is presented. Since embedding in a transform domain improves security, the proposed method uses Reversible Walsh-Hadamard Transform (RWHT) to commute the host image. Afterward, Singular Value Decomposition (SVD) technique is performed on the transformed image for watermark embedding. For a full recovery, additional information is encoded using Quick Response (QR) code, which is embedded by a prediction-based method. To evaluate the performance of the proposed method, a set of comparative experiments is done. The obtained results confirm the effectiveness of the proposed method in both visual quality and capacity.

Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wavelet based image denoising, selecting a proper model for wavelet coefficients is very important. In this paper, we model wavelet coefficients in each sub-band by heavy-tail distributions that are from scale mixture of normal distribution family. The parameters of distributions are estimated adaptively to model the correlation between the coefficient amplitudes, so the intra-scale dependency of wavelet coefficients is also considered. The denoising results confirm the effectiveness of the proposed method.

Reversible watermarking is a special kind of the lossless data hiding techniques which allows lossless recovering of both the watermark and the host image. In this paper, a new reversible watermarking scheme based on error prediction in Hadamard domain is presented. In the proposed method, the original image is divided into blocks and transformed to Hadamard domain. If a block is in a smooth area, its AC coefficients will be predicted using a linear predictor function. Then the value of error between the original and the predicted coefficient is computed. At last, a watermark bit will be embedded in the error. To reduce the error value, an Adaline neural network is used to determine coefficients of the predictor function. The experimental results show that the proposed method provides higher capacity and quality in comparison to some well-known methods.

In the gradient dependent denoising methods based on partial differential equation, the process of denoising is controlled through the gradient operation. Hence, the edges are preserved while texture and fine details (having oscillatory nature, the same as noise) are degraded. This paper proposes an algorithm which adaptively selects diffusion coefficient using the residual local power and the amount of the gradient magnitude. Since texture regions correspond to large values of the local power of the residue, this strategy permits to simultaneously preserve the edges, textures, and fine details. To evaluate the proposed method, a variety of experiments are carried out confirming the performance of the proposed algorithm with respect to peak signal-to-noise ratio, mean structural similarity, universal quality index, visual information fidelity and visual quality.

Over the course of the last two decades, secure communication has become a very important issue due to the rapid growth of information technology and the development of public communication networks in which digital images are widely transmitted. In this paper, logistic map was employed for the encryption of gray-scale images. The proposed algorithm, demonstrating a proper performance according to the experimental results, divides the image into blocks and encrypts them with XOR operation and chaotic windows. Moreover, it has a large key space and the resulted encrypted images have homogeneous histograms. The large-enough NPCR and UACI of this algorithm indicate its resistance to differential attacks, not to mention the fact that it is suitable for noisy communication networks and could be made use of in parallel processing. (C) 2017 Elsevier Ltd. All rights reserved.

This paper aims to optimize multi-wavelength Brillouin-Raman fiber laser (MBRFL) utilizing nonlinear amplifying loop mirror (NALM) design through employment a simple genetic algorithm. This is carried out in order to evaluate large degree of freedom of parameters through transmitted power as a fitness function that can be exploited to optimize the behavior of the NALM without repeating experiment. The genetic algorithm is intelligent enough that can support different parameters to achieve maximum transmitted power of 35 mW. This is attained when the optimized parameters including 5 km of dispersing compensating fiber length with Raman pump power of 600 mW and attenuator value of -30 dB are incorporated.

In this paper, a new class of step edge detection IIR filters derived from the Shen–Castan and Deriche edge detection filters is proposed. To avoid the discontinuity drawback of Shen–Castan edge detector, we multiply its impulse response by a proper function. This function exhibits a behavior closed to the sign(.) for large values of x, and similar to the line f(x) = k.x, for small values of x. Hence, the new edge detector preserves good behaviors of both Deriche and Shen–Castan operators, while a detection-localization product larger than 2 is achieved. Furthermore, it is shown that the proposed edge detector is optimal according to Canny’s criteria. In addition, a recursive implementation of the new operator is proposed that provides a fast edge detection algorithm. Experimental results confirm the high performance of the proposed edge detector.

Eye Detection has an important role in the field of biometric identification and known as one method of person's identification. In recent years, many efforts have been done which can detect eye automatically and with different image conditions. However, each method has its own drawbacks which can control some of these conditions. In this paper, different methods of eye detection will be categorized and explained. In each category, the advantages and disadvantages of each method will be presented.

Diffusion coefficient has an important role in the performance of partial differential equation (PDE) based image denoising techniques. Commonly, the classical Perona–Malik (PM) diffusion coefficient is widely used in PDE-based noise removal algorithms. In this paper, PM diffusion coefficient is analyzed regarding to its flux. Based on the analysis, PM flux for regions where the gradient magnitude is higher than smoothing threshold may lead to undesirable blurring effect and edge displacement. To address these issues, the image is divided into three segments based on the gradient magnitude: regions where the gradient is lower than the smoothing threshold, regions where the gradient is between the smoothing threshold and inflection point of flux, and regions where the gradient magnitude is higher than inflection point. We define the conditions that should be considered in these three segments. Then, a diffusion coefficient, satisfying all these conditions, is computed. Experimental results confirm the performance of the proposed method with regard to peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM), universal quality index (UQI), visual information fidelity (VIF), feature similarity (FSIM), information content weighted SSIM (IW-SSIM) and visual quality.

In this paper, a new robust data clustering algorithm inspired by Newtonian law of gravity is proposed. The proposed algorithm not only reduces the effects of noise and outliers but also, it is not sensible to the initial positions of the centroids. In the proposed method, data points and the cluster centroids are considered as fixed celestial objects and movable objects, respectively. The celestial objects apply a gravity force to the movable objects and change their positions in the feature space and therefore, the best positions of the cluster centroids are determined by employing the law of gravity. To evaluate the performance of the proposed algorithm, a comparative experimental study with some well-known clustering algorithms, using three visual datasets as well as several benchmark datasets from UCI, is performed. The experimental results confirm the effectiveness and the efficiency of the proposed clustering algorithm.

Today the security of images has become increasingly important in the realm of information technology (IT). A secure image encryptor must have a pseudo-noise output for each input and resist various attacks. In this paper, an algorithm was proposed in terms of encrypting colour images using chaotic logistic map and sum operation modulo 4 and 256. Generally speaking, in this algorithm the sum operation is first conducted on the pixels of the image employing a two-step modulo 256; the resulting image is then divided into four sub-matrices each of which is converted into modulo 4. The sum process modulo 4 is then conducted on these four sub-matrices and four other random sub-matrices. The pixels are finally permuted using the chaotic map. Our simulation results and the comparison with similar works showed that this algorithm has appropriate resistance against static and differential attacks as it has the three important characteristics of permutation, substitution, and diffusion. Its pseudo-noise output is largely dependent upon the key, and it also provides a safe key space to prevent brute force attack.

In this paper a multi-parameter A*(A- star)-ants based algorithm is proposed in order to find the best optimized multi-parameter path between two desired points in regions. This algorithm recognizes paths, according to user desired parameters using electronic maps. The proposed algorithm is a combination of A* and ants algorithm in which the proposed A* algorithm is the prologue to the suggested ant based algorithm .In fact, this A* algorithm invigorates some paths pheromones in ants algorithm. As one of implementations of this method, this algorithm was applied on a part of Kerman city, Iran as a multi-parameter vehicle navigator. It finds the best optimized multi-parameter direction between two desired junctions based on city traveler parameters. Comparison results between the proposed method and ants algorithm demonstrates efficiency and lower cost function results of the proposed method versus ants algorithm.

Content based image retrieval (CBIR) systems could provide more precise results by taking the user’s feedbacks into account. Two types of the relevance feedback learning paradigms are short term learning (STL) and long term learning (LTL). By using both STL and LTL, a collaborative CBIR system is proposed in this paper. The proposed system introduced three fusion methods: including fusion in retrieved images, fusion in ranks, and fusion in similarities to make cooperation between STL and LTL. The proposed fusion methods are examined in a CBIR system equipped with a proposed statistical semantic clustering (SSC) method of LTL. The SSC method works based on the concept of semantic categories of the images by clustering techniques and constructing a relevancy matrix between images and semantic categories. The results of the SSC method with the suggested fusion methods are compared with two state-of-the-art LTL methods, namely virtual feature based method and dynamic semantic clustering. Comparative results confirm the efficiency of the proposed method. Furthermore, experimental results demonstrate that for a unique LTL method, various fusion methods lead to different results.

This paper presents a new relevance feedback approach based on similarity refinement. In the proposed approach weight correction of feature’s components is done by a proposed rule set using mean and standard deviation of feature vectors of relevant (positive) and irrelevant (negative) images. Also, the weight of each type of features is adjusted according to the relevant images’ rank in the retrieval based on only the same type of feature. To evaluate the performance of the proposed method, a set of comparative experiments on a general database containing 20,000 images of various semantic groups are performed. The results confirm the effectiveness of the proposed method comparing with two well-known methods.

Relevance feedback is a powerful tool emerged to boost the retrieval performance of content based image retrieval (CBIR) systems. Short term learning (STL) and long term learning (LTL) are two learning methods of relevance feedback scheme. This paper presents a long term learning method in CBIR systems adopting case based reasoning (CBR) which is called Case-based LTL (CB-LTL). The method has two stages of learning and reasoning. In the learning stage, information extracted from retrieval sessions is saved as cases and in the reasoning stage, information of cases is utilized to improve the results of the retrieval sessions. The main components of CB-LTL method are 'key of query' which represents the desire of the user, a 'trigger function' which is used to find a similar case with a query, and 'semantic frame' which is a structure for saving cases. In the proposed method, cases are recorded in the case knowledge base using both low level and high level features. The information of the relevance feedback and short term learning are employed as high level features. In this paper, the general approach of CB-LTL is produced and an example of the method is implemented in a CBIR system with the similarity refinement based STL. To evaluate the proposed method, a comparative study with the "virtual feature based" LTL method is performed based on the Corel image dataset. The experimental results validate the effectiveness of Case-based LTL method empirically.