ali aghagolzadeh

In pansharpening, a low-resolution multispectral image (LRMS) and a high-resolution panchromatic image (PAN) are fused to produce a high-resolution multispectral image (HRMS). Recent studies have shown that convolutional neural networks can be used for sharpening remote sensing images with excellent results. However, there are still two major problems that need to be resolved. Despite the lack of ideal HRMS images for learning, most current methods require more effort to generate simulated data. On the other hand, these methods usually ignore the rich spatial information in panchromatic images. To address these issues, we have proposed an unsupervised fusion framework based on a multiscale dense network called UMP-GAN for pansharpening. This framework, which employs generative adversarial networks, can be trained directly on full-resolution images without requiring any preprocessing step. First, a multiscale dense generator network is proposed to extract features from original input images to generate HRMS images. In the following, two separate discriminator networks are used to protect the spectral information and spatial details of the input images in the final image. Therefore, the proposed method provides the possibility of training two discriminating networks, each of which has a different and complementary task. Finally, new lost functions are proposed to perform training under unsupervised settings. This method can deepen the exchange of gradient information between the generator network and the discriminator networks. The results from WorldView-2, GaoFen-2, and QuickBird satellite images show that the proposed method performs better than other state-of-the-art models in the fusion of remote sensing images.

Recently, a method for multi-exposure images fusion based on structural decomposition of images into three parts including signal strength, signal structure and signal mean has been introduced. In this paper, we seek to use this decomposition, for images fusion in other fields, including multimodal medical, multi-focus, and infrared and visible images. To increase the fusion quality, besides the introduction of the proposed weighting factor in the structural decomposition, contourlet transformation and the pyramidal structure have also been used. First, each of the K input images are represented into low frequency and high frequency subbands, by using contourlet transform. Then, all the corresponding subbands (resulting from the same scales and directions) are fused with each other, separately and in an iterative process. In this iterative process, first, a separate pyramid structure (including approximation and detail layers) is created for each of the corresponding K subbands. These layers are obtained by the down-sampling of subbands and structural separation based on the proposed new weighting factor. Then, the fusion is performed in the reverse direction of the pyramidal structure and the fused image of the K corresponding subband is obtained. By repeating this process, the fused image will be obtained for all the corresponding subbands. At the end, the final fused image is obtained by the inverse contourlet transformation on the fused images of the subbands. Several visual and quantitative comparisons, with 7 common methods in this field, have been made. In the visual aspect, the proposed method shows the highest quality.

Learning and reconstruction-based methods are the two main approaches to the solve single image super resolution (SISR) problem. In this paper, to exploit the advantages of both learning based and reconstruction based approaches, we propose a new SISR framework by combining them, which can effectively utilize their benefits. The external directional dictionaries (EDD) are learned from external high quality images. Additionally, we embeded the nonlocal means (NLM) filter and an isotropic total variation (TV) scheme in the reconstruction based method. We suggest a new supervised clustering scheme via curvelet based direction extraction method (CCDE) to learn the external directional dictionaries from candidate patches with sharp edges. Each input patch is coded by all the EDD. Each of the reconstructed patches under different EDD is applied with a weighted penalty to characterize the given input patch. To disclose new details, the local smoothness and nonlocal self-similarity priors are added on the recovered patch by TV scheme and NLM filter. Extensive experimental results validate the effectiveness and robustness of the proposed method comparing with the state-of- the-art algorithms in SISR methods. Our proposed schemes can retrieve more fine structures and obtain superior results than the competing methods with the scaling factors of 2 and 3.

Nowadays, unique identifiers are used to authenticate individuals. These identifiers are called biometrics with iris being one of the most popular ones. Research performed in this paper consist of designing a new identification system based on improved security in iris database. In this paper, we propose an iris template protection method based on Visual Cryptography (VC) and RSA. For iris template protection, the binary iris template is divided into two shares using VC, where one share is stored in the database and the other is kept with the user on a smart    card. In order to provide additional security, the share that is stored in the database is encrypted with RSA encryption. In the next step the patterns obtained from each image are compared with the patterns in the database and, by defining a threshold, the identification procedure is carried out. In this paper, the CASIA - v1 image bank has been used. The EER value after encryption is approximately 0.01. We then expose the system to different noises such as salt and pepper, white Gaussian noise and mean filt.r. After applying the noises, we haveit is observed that the system has shows been better performance compared to the than other systems which we have investigated.

Local smoothness and nonlocal self-similarity of natural images are two main priors in single image super resolution (SISR) problem. Although local sparsity is efficiently utilized to describe the local smoothness, but ignoring the correlation between the sparse representation coefficients of similar patches can lead to inaccurate sparse coding coefficients. In this paper, we propose the method that enforce the local smoothness and nonlocal self-similarity by sparse representation in a unified framework, called adaptive group-based sparse domain selection (A-GSDS). Nonlocal patches with similar structures are exploited and stacked in the form of matrix as the basic unit of sparse representation called group. These groups are converted into a column vector, each column selects the best fitted PCA sub dictionary which is learned from the training data. The sparse coding process for each column in the domain of group leads to find sparse vectors which can be easily estimated by the selected orthogonal sub dictionaries. To further improve the performance of the group-based sparse representation, we use nonlocal means regularization term.  Extensive experimental results validate the effectiveness of the proposed method comparing with the state-of- the-art algorithms.

Blind recognition of error correction codes parameters from intercepted bit-stream at the receiver side, is highly considered in military and commercial applications. In fact, identification of the encoding scheme used in the transmitter without any prior information, is a challenging task to the adversary. Several methods have been presented for blind code recognition. In this paper, a statistical method for recognition of  the length of the code word and the length of the block of information is presented. This scheme not only is resistant to error, but also its performance sustains in long codes. In this work, the method has been tested using some clustering algorithms such as K-Means and Jenks Natural Breaks. Then, a novel method to extract features of systematic binary linear block codes has been presented. Simulation results in MATLAB show that the proposed method, in addition to having low computational complexity and high performance rate, have an acceptable result in identifying systematic block codes with long lengths and even at high error levels.

Visual cryptography is one of the newest techniques is image encryption. Visual cryptography encrypts visual information and generates two or more shares which contain no information separately, but reveal the secret when superposed. The main advantage of this scheme is that the decoding process does not need any knowledge of cryptography and human visual system is able to decrypt the secret message. In this article, a new encryption method based on particle swarm optimization is provided. The basis matrices are obtained using this approach and then used for encryption. In previous work, a Genetic Algorithm based method was proposed. In our work we fix a subtle yet crucial bug in the GA based method which is generating pictures with negative contrast in some cases and propose a simpler alternative. Simulation results show that the proposed method meets all the initial conditions of visual cryptography while decreasing the number of function evaluations and can provide a contrast enhancement. Moreover, the proposed method has the advantage of being generic and can be used in various threshold based visual cryptography.

Feature selection is an important step in most classification problems to select an optimal subset of features to increase the learning accuracy and reduce the computational time. In this paper we proposed a new feature clustering based method to perform feature selection (FFS) in classification problems. The FFS algorithm works in two steps. In the first step, features are divided into clusters by using F-DBSCAN method. A novel F-DBSCAN clustering method used mutual information for measuring dependencies between features. In the second step, the most representative feature is selected from each cluster by a new criterion function. This allows us to consider the possible dependency on the target class and the redundancy between the selected features in each cluster. The experimental results on different datasets show that the proposed algorithm is more effective for feature selection in classification problems.. Compared with the other methods, the average classification accuracy of C4.5, KNN and Naïve Bayes are improved using FFS by 8.05, 8.36 and 4.63 percent, respectively. Also, the results demonstrate that the FFS algorithm produces small subsets of features with very high classification rate.

In this paper, attempting to improve the recognition rate and solve some problems such as pose, lighting variations and partial occlusion in video sequences using Orthogonal Locality Preserving Projection (OLPP). In this research, first of all face in video frames is detected for background removing. Then each set of images is distributed on a nonlinear manifold and clustered using appropriate methods then the center of each cluster is considered as the cluster representative. It is also shown that by using OLPP the key frames are projected to the new space, where the frames in each manifold are better closed also the frames in different manifold are better separated. The recognition is done by projecting the test video sequence to the new space and calculating the distance between manifolds. Comparing the experimental results of the proposed method with other methods demonstrate the effectiveness of the proposed approach.

This paper presents a new approach for modeling the normal behaviors and detecting the abnormal behaviors. The approach consists of several main steps. First, using a detection method, the foreground and background regions are separated. Then, the busy-idle rates are defined as the behavioral features and, based on these features, a behavioral model is extracted for each pixel block. In the following, spectral clustering is used to classify the normal behaviors on the condition that a set of normal data is provided. In the classification process, the pixel blocks with similar behaviors are grouped together. A behavioral model is defined for each group of the blocks with similar behaviors. The behavioral model adopted in this paper is Hidden Markov Model. The results of the obtained classification and normal behaviors are used to detect the abnormal behaviors; i.e., based on the normal-behavior model for each cluster, if the observation sequence probability given by the normalbehavior model is lower than the threshold level, the pixel block is identified as the region in which the abnormal behaviors happened. The experimental results obtained from video data confirm the efficiency, accuracy, and speed of the approach adopted in this paper.

Tracking has considered many years ago and it has been concerned as an unsolved problem. This paper presents a methods using Genetic algorithm. The proposed algorithm uses background subtraction and multi scale LBP to detect moving target region. We have used Chi-square Criterion to calculate the similarity between two regions. The simulation results shows that the proposed method is efficient in accuracy and speed. In each frame the average time for calculating the position of the vehicle is 0.33 seconds while the sequential search time is 02.15 seconds.

In this paper, we propose a new error concealment method for covering up the high packet losses of an original image after its transmission through an error-prone channel. Our proposed technique utilizes Discrete Wavelet Transform (DWT) to produce a lower resolution copy of the original image. Then we embed some of discrete cosine transform (DCT) coefficients of this downsized replica of the original image into the original image in the spatial (pixel) domain inside macroblocks (MBs) as a watermark. This technique can be implemented for error-prone channels to combat degradations due to packets loss in a backward-compatible scheme.