Sharif Digital Repository

This article introduces a new adaptive method for image interpolation. In order to obtain a high resolution (HR) image from its low resolution (LR) counterpart (original image), an interpolator function (array) is used, and the main focus of this manuscript is to formulate and define this function. By applying this interpolator function to each row and column of a LR image, it is possible to construct its HR counterpart. One of the main challenges of image interpolation algorithms is to maintain the edge structures while developing an HR image from the LR replica. The proposed approach overcomes this challenge and exhibits remarkable results at the image edges. The peak signal to noise ratio... 

In this paper we extended a previously investigated modular method that is designed to compensate for interpolation distortions of one-dimensional signals, to two dimensions (2-D). Next the proposed 2-D modular technique was applied in an iterative fashion and was shown through both simulations and theoretical analyses to enhance the convergence of the iterative technique. In fact, with only a few modules we were able to achieve drastic improvements in signal reconstruction, and with a much less computational complexity. Moreover, both the simulations and the theoretical analysis confirmed the robustness of the proposed scheme against additive noise  

Background: The issue of medial image resolution enhancement is one of the most important topics for medical imaging that helps improve the performance of many post-processing aspects like classification and segmentation towards medical diagnosis. Objective: Our aim in this paper is to evaluate different types of pixel selection models in terms of pixel originality in medical image reconstruction problems. A previous investigation showed that selecting far original pixels has highly better performance than using near unoriginal/estimated pixels while magnifying some benchmarks in digital image processing. Material and Methods: In our technical study, we apply two classical inter-polators,... 

Background and objective Registration is a critical step in group analysis of diffusion weighted images (DWI). Image registration is also necessary for construction of white matter atlases that can be used to identify white matter changes. A challenge in the registration of DWI is that the orientation of the fiber bundles should be considered in the process, making their registration more challenging than that of the scalar images. Most of the current registration methods use a model of diffusion profile, limiting the method to the used model. Methods We propose a model-independent method for DWI registration. The proposed method uses a multi-level free-form deformation (FFD), a sparse... 

Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive method for investigating the brain white matter structure. It can be used to evaluate fiber bundles in the brain but in the regions with crossing fibers, it fails. To resolve this problem, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding directions is used and for reconstruction, the Q-ball method is applied. In this method, orientation distribution function (ODF) of fibers can be calculated. Mathematical models play a crucial role in the field of ODF. For instance, in registering Q-ball images for applications like group analysis or atlas construction, one needs to interpolate... 

A new method for image registration has been previously proposed by the authors, which the registration is based on physical forces. The registration parameters are translation and rotation. This method assumes images like charged materials that attract each other. In this case, one of the images moves in the same direction as the applied force while the other one is still. The movement of the image continues until the resultant force becomes zero. This approach estimates the registration parameters simultaneously and leading to a better optimized set of registration parameters. The registration error for this method is 1 to 3 pixels. In this paper we aim to develop this method for the... 

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...