Sharif Digital Repository

During the past two decades, the lattice Boltzmann (LB) method has been introduced as a class of computational fluid dynamic methods for fluid flow simulations. In this method, instead of solving the Navier Stocks equation, the Boltzmann equation is solved to simulate the flow of a fluid. This method was originally developed based on uniform grids. However, in order to model complex geometries such as porous media, it can be very slow in comparison with other techniques such as finite differences and finite elements. To eliminate this limitation, a number of studies have aimed to formulate the lattice Boltzmann on the unstructured grids. This paper deals with simulating fluid flow through a... 

We propose an algorithm based on mutual information to address the problem of online reconstruction of dynamic MRI from partial k-space measurements. Most of previous compressed sensing (CS) based methods successfully leverage sparsity constraint for offline reconstruction of MR images, yet they are not used in online applications due to their complexities. In this paper, we formulate the reconstruction as a constraint optimization problem and try to maximize the mutual information between the current and the previous time frames. Conjugate gradient method is used to solve the optimization problem. Using Cartesian mask to undersample k-space measurements, the proposed method reduces... 

Using a second receiver antenna close to the main transceiver antenna of inverse synthetic aperture radar (ISAR), it is possible to find 3-D positions of target scattering points. Such system is called bistatic, monopulse, or interferometric ISAR (InISAR). In the conventional model of ISAR, the unknown flying object should have a linear trajectory, and only small deviations from this trajectory can be compensated. Target motions which are highly nonlinear or curvy cannot be used in the conventional model. In this paper, we propose a new model for InISAR to process all collected data from the target, regardless of the form of the flight path. More accuracy is achieved for 3-D positioning of... 

Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems. SISO systems as associative layer of algorithm capture partial spatial knowledge of sample data space, and enable a granular knowledge resolution tuning mechanism through the learning process. The... 

During the past two decades, the lattice Boltzmann (LB) method has been introduced as a class of computational fluid dynamic methods for fluid flow simulations. In this method, instead of solving the Navier Stocks equation, the Boltzmann equation is solved to simulate the flow of a fluid. This method was originally developed based on uniform grids. However, in order to model complex geometries such as porous media, it can be very slow in comparison with other techniques such as finite differences and finite elements. To eliminate this limitation, a number of studies have aimed to formulate the lattice Boltzmann on the unstructured grids. This paper deals with simulating fluid flow through a... 

Reducing the time required for MRI, has taken a lot of attention since its inventions. Compressed sensing (CS) is a relatively new method used a lot to reduce the required time. Usage of ordinary compressed sensing in MRI imaging needs conversion of 2D MRI signal (image) to 1D signal by some techniques. This conversion of the signal from 2D to 1D results in heavy computational burden. In this paper, based on separable transforms, a method is proposed which enables the usage of CS in MRI directly in 2D case. By means of this method, imaging can be done faster and with less computational burden  

In this paper, we propose an information theoretic approach to fuse images compressed by compressed sensing (CS) techniques. The goal is to fuse multiple compressed images directly using measurements and reconstruct the final image only once. Since the reconstruction is the most expensive step, it would be a more economic method than separate reconstruction of each image. The proposed scheme is based on calculating the result using weighted average on the measurements of the inputs, where weights are calculated by information theoretic functions. The simulation results show that the final images produced by our method have higher quality than those produced by traditional methods, especially... 

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods... 

Sparse representation is a powerful tool for image processing, including noise removal. It is an effective method for Gaussian noise removal by taking advantage of a fixed and learned dictionary. In this study, the variable distribution of Rician noise is reduced in magnetic resonance (MR) images by sparse representation based on reconstruction error sets. Standard deviation of Gaussian noise is used to find these errors locally. The proposed method represents two formulas for local error calculation using standard deviation of noise. The acquired results from the real and simulated images are comparable, and in some cases, better than the best Rician noise removal method due to the... 

Electrical Impedance Tomography (EIT), is one of the medical imaging technologies. It can also be used in industrial process monitoring. In this method, the image of the electrical conductivity distribution of the inner part of a conductive subject can be reconstructed. The image reconstruction process is done by injecting an accurate current into the boundary of a conductive subject (e.g. body), measuring the voltages around the boundary and transmitting them to a computer, and processing on acquired data with a software (e.g., MATLAB). The images are obtained from the peripheral data by using an algorithm. Precise EIT instrumentation plays an important role in the final images quality. In... 

This paper presents an efficient scheme for blind watermark attacking using the concept of matching of the long-range data. The main idea of the proposed attack is to add plenty of noise to the watermarked image and then try to restore an unwatermarked copy of the noisy image. The aim is to destroy the watermark information without accessing the parameters used during the watermark embedding process. So, it allows our approach to be completely free from any pre-assumption on the watermarking algorithm or any other parameters that is used during the watermark embedding procedure. Experimental results show the proposed algorithm's superiority over several other traditional watermarking... 

Medical images contain information about vital organic tissues inside of human body and are widely used for diagnoses of disease or for surgical purposes. Image reconstruction is essential for medical images for some applications such as suppression of noise or de-blurring the image to provide images with better quality and contrast. In this paper Kalman filter technique is introduced for medical image reconstruction. This technique is operated in time domain unlike the filtered back projection which operates in frequency domain. Results indicated that as the number of projection increases in both the collected ray sum corrupted by noise and by blurring the quality of reconstructed image... 

Remote Sensing (RS) images or satellite images include information about earth. Compression of RS images is important in the field of satellite transmission systems and mass storage purposes. Because of importance of information and existent of large amount of details, lossless compression preferred. Real time compression technique is applied on satellite and aerial transmission systems [1]. A simple algorithm accelerates the whole process in real time purposes. Lossless JPEG, JPEG-LS and JPEG2000 are some famous lossless compression methods. Transformation is the first step of these methods. In this paper, a simple and efficient method of lossless image transformation has been introduced by... 

In this paper, a Kalman filter technique which is operated in time is introduced for noise reduction on CT set of projections to reconstruct medical images. The experiments were done on medical image of kidneys and the simulated projections are captured by CT scanner. Evaluation results indicated that as the number of projections increase in the collected ray sums corrupted by noise the quality of reconstructed image becomes better in terms of contrast and transparency. However, for the comparison issue, the same conditions are applied for reconstruction of medical image in frequency domain using filter back projection technique. It observes that filter back projection technique does not... 

As an alternative to adaptive nonlinear schemes for dimensionality reduction, linear random projection has recently proved to be a reliable means for high-dimensional data processing. Widespread application of conventional random projection in the context of image analysis is, however, mainly impeded by excessive computational and memory requirements. In this paper, a two-dimensional random projection scheme is considered as a remedy to this problem, and the associated key notion of concentration of measure is closely studied. It is then applied in the contexts of image classification and sparse image reconstruction. Finally, theoretical results are validated within a comprehensive set of... 

Medical images contain information about vital organic tissues inside of human body and are widely used for diagnoses of disease or for surgical purposes. Due to vital rule of image reconstruction in medical sciences the corresponding algorithms with better efficiency and higher speed is desirable. Most algorithms in image reconstruction are operated on frequency domain such as filtered back projection. In this paper, a Kalman filter technique which is operated in time domain is introduced for reconstruction of CT medical images. Results indicated that as the number of projection increases in both normal collected ray sum and the collected ray sum corrupted by noise the quality of... 

In this paper, we address the problem of recovering degraded images using multivariate Gaussian mixture model (GMM) as a prior. The GMM framework in our method for image restoration is based on the assumption that the accumulation of similar patches in a neighborhood are derived from a multivariate Gaussian probability distribution with a specific covariance and mean. Previous methods of image restoration with GMM have not considered spatial (geometric) distance between patches in clustering. Our conducted experiments show that in the case of constraining Gaussian estimates into a finite-sized windows, the patch clusters are more likely to be derived from the estimated multivariate Gaussian...